# Joint Multi-Pitch Detection Using Harmonic Envelope Estimation for Polyphonic Music Transcription

**ABSTRACT** In this paper, a method for automatic transcription of music signals based on joint multiple-F0 estimation is proposed. As a time-frequency representation, the constant-Q resonator time-frequency image is employed, while a novel noise suppression technique based on pink noise assumption is applied in a preprocessing step. In the multiple-F0 estimation stage, the optimal tuning and inharmonicity parameters are computed and a salience function is proposed in order to select pitch candidates. For each pitch candidate combination, an overlapping partial treatment procedure is used, which is based on a novel spectral envelope estimation procedure for the log-frequency domain, in order to compute the harmonic envelope of candidate pitches. In order to select the optimal pitch combination for each time frame, a score function is proposed which combines spectral and temporal characteristics of the candidate pitches and also aims to suppress harmonic errors. For postprocessing, hidden Markov models (HMMs) and conditional random fields (CRFs) trained on MIDI data are employed, in order to boost transcription accuracy. The system was trained on isolated piano sounds from the MAPS database and was tested on classic and jazz recordings from the RWC database, as well as on recordings from a Disklavier piano. A comparison with several state-of-the-art systems is provided using a variety of error metrics, where encouraging results are indicated.

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**ABSTRACT:**An automatic music transcriber is a device that detects, without human interference, the musical gestures required to play a particular piece. Many techniques have been proposed to solve the problem of automatic music transcription. This paper presents an overview on the theme, discussing digital signal processing techniques, pattern classification techniques and heuristic assumptions derived from music knowledge that were used to build some of the main systems found in the literature. The paper is focused on the motivations behind each technique, aiming to serve both as an introduction to the theme and as resource for the development of new solutions for automatic transcription.Journal of the Brazilian Computer Society 11/2013; 19(4):589-604. - [Show abstract] [Hide abstract]

**ABSTRACT:**Conditional random fields (CRFs) are probabilistic sequence models that have been applied in the last decade to a number of applications in audio, speech, and language processing. In this paper, we provide a tutorial overview of CRF technologies, pointing to other resources for more in-depth discussion; in particular, we describe the common linear-chain model as well as a number of common extensions within the CRF family of models. An overview of the mathematical techniques used in training and evaluating these models is also provided, as well as a discussion of the relationships with other probabilistic models. Finally, we survey recent work in speech, audio, and language processing to show how the same CRF technology can be deployed in different scenarios.Proceedings of the IEEE 01/2013; 101(5):1054-1075. · 6.91 Impact Factor - SourceAvailable from: François Rigaud
##### Conference Paper: Piano sound analysis using Non-negative Matrix Factorization with inharmonicity constraint

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**ABSTRACT:**This paper presents a method for estimating the tuning and the inharmonicity coefficient of piano tones, from single notes or chord recordings. It is based on the Non-negative Matrix Factorization (NMF) framework, with a parametric model for the dictionary atoms. The key point here is to include as a relaxed constraint the inharmonicity law modelling the frequencies of transverse vibrations for stiff strings. Applications show that this can be used to finely estimate the tuning and the inharmonicity coefficient of several notes, even in the case of high polyphony. The use of NMF makes this method relevant when tasks like music transcription or source/note separation are targeted.Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European; 01/2012

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING

copyrighted components of this work in other works.

Published version: IEEE Journal of Selected Topics in Signal Processing, 5(6):1111-1123, Oct. 2011. doi: 10.1109/JSTSP.2011.2162394

1

Joint Multi-pitch Detection using Harmonic

Envelope Estimation for Polyphonic Music

Transcription

Emmanouil Benetos, Student Member, IEEE and Simon Dixon

Abstract—In this paper, a method for automatic transcrip-

tion of music signals based on joint multiple-F0 estimation is

proposed. As a time-frequency representation, the constant-Q

resonator time-frequency image is employed, while a novel noise

suppression technique based on pink noise assumption is applied

in a preprocessing step. In the multiple-F0 estimation stage, the

optimal tuning and inharmonicity parameters are computed and

a salience function is proposed in order to select pitch candidates.

For each pitch candidate combination, an overlapping partial

treatment procedure is used, which is based on a novel spectral

envelope estimation procedure for the log-frequency domain, in

order to compute the harmonic envelope of candidate pitches.

In order to select the optimal pitch combination for each time

frame, a score function is proposed which combines spectral and

temporal characteristics of the candidate pitches and also aims

to suppress harmonic errors. For postprocessing, hidden Markov

models (HMMs) and conditional random fields (CRFs) trained

on MIDI data are employed, in order to boost transcription

accuracy. The system was trained on isolated piano sounds

from the MAPS database and was tested on classic and jazz

recordings from the RWC database, as well as on recordings

from a Disklavier piano. A comparison with several state-of-the-

art systems is provided using a variety of error metrics, where

encouraging results are indicated.

Index Terms—Automatic music transcription, Harmonic en-

velope estimation, Conditional random fields, Resonator time-

frequency image

I. INTRODUCTION

A

using some form of musical notation. Even for expert musi-

cians, transcribing polyphonic pieces of music is not a trivial

task, and while the problem of automatic pitch estimation

for monophonic signals is considered to be a solved prob-

lem, the creation of an automated system able to transcribe

polyphonic music without setting restrictions on the degree

of polyphony and the instrument type still remains open. In

the past years, the problem of automatic music transcription

has gained considerable research interest due to the numerous

applications associated with the area, such as automatic search

and annotation of musical information, interactive music sys-

tems (i.e. computer participation in live human performances,

score following, and rhythm tracking), as well as musicolog-

ical analysis [1]–[3]. Important subtasks for automatic music

UTOMATIC music transcription is the process of con-

verting an audio recording into a symbolic representation

The authors are with the Queen Mary University of London, Centre

for Digital Music, School of Electronic Engineering and Computer Sci-

ence, E1 4NS London, U.K. (e-mail: emmanouilb@eecs.qmul.ac.uk; si-

mond@eecs.qmul.ac.uk).

transcription include pitch estimation, onset/offset detection,

loudness estimation, instrument recognition, and extraction

of rhythmic information. For an overview on transcription

approaches, the reader is referred to [3], while in [4] a review

of multiple fundamentalfrequencyestimation systems is given.

Proposed methods for automatic transcription can be orga-

nized according to the various techniques or models employed.

A large subset of the proposed systems employ signal process-

ing techniques, usually for feature extraction, without resorting

to any supervised or unsupervised learning procedures or

classifiers for pitch estimation (see [3] for an overview).

Several approaches for note tracking have been proposed

using variants of non-negativematrix factorization (NMF), e.g.

[5]. Maximum likelihood approaches, usually employing the

expectation-maximization algorithm, have been also proposed

in order to estimate the spectral envelope of candidate pitches

or to estimate the likelihood of a set of pitch candidates

(e.g. [2], [6]). Hidden Markov models (HMMs) are frequently

used in a postprocessing stage for note tracking, due to the

sequential structure offered by the models (e.g. [7], [8]).

Approaches for transcription related to the current work

are discussed here. Yeh et al. in [9] present a multipitch

estimation algorithm based on a pitch candidate set score

function. The front-end of the algorithm consists of an STFT

computation followed by an adaptive noise level estimation

method based on the assumption that the noise amplitude

follows a Rayleigh distribution. Given a pitch candidate set,

the overlapping partials are detected and smoothed according

to the spectral smoothness principle. The weighted score

function consists of 4 features: harmonicity, mean bandwidth,

spectral centroid, and synchronicity. A polyphony inference

mechanism based on the score function increase selects the

optimal pitch candidate set. Zhou [10] proposed an iterative

method for polyphonic pitch estimation using a complex

resonator filterbank as a front-end, called resonator time-

frequency image (RTFI). F0 candidates are selected according

to their pitch energy spectrum value and a set of rules is

utilized in order to cancel extra estimated pitches. These rules

are based on the number of harmonic components detected

for each pitch and the spectral irregularity measure, which

measures the concentrated energy around possibly overlapped

partials from harmonically-related F0s.

A probabilistic method is proposed by in [6], where pi-

ano notes are jointly estimated using a likelihood function

which models the spectral envelope of overtones using a

smooth autoregressive (AR) model and models the residual

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2IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING

noise using a low-order moving average (MA) model. The

likelihood function is able to handle inharmonicity and the

amplitudes of overtones are assumed to be generated by a

complex Gaussian random variable. In [7], Poliner and Ellis

used STFT bins for frame-level piano note classification using

one-versus-all support vector machines (SVMs). In order to

improve transcription performance, the classification output of

the SVMs was fed as input to HMMs for post-processing.

Finally, previous work by the authors includes an iterative

system for multiple-F0 estimation for piano sounds [11] which

incorporates temporal information for pitch estimation based

on the common amplitude modulation (CAM) assumption

and a public evaluation of the aforementioned system for

the MIREX 2010 multiple fundamental frequency estimation

task [12]. Results for the MIREX task were encouraging,

considering that the system was trained on isolated piano

sounds and tested on woodwind and string recordings, noting

also that no note tracking procedure was incorporated.

In this work, a system for automatic transcription is pro-

posed which is based on joint multiple-F0 estimation and

subsequent note tracking. The constant-Q RTFI is used as

a suitable time-frequency representation for music signals

and a noise suppression method based on cepstral smoothing

and pink noise assumption is proposed. For the multiple-

F0 estimation step, a salience function is proposed for pitch

candidate selection that incorporates tuning and inharmonicity

estimation. For each possible pitch combination, an overlap-

ping partial treatment procedure is proposed that is based on

a novel method for spectral envelope estimation in the log-

frequency domain, used for computing the harmonic envelope

of candidate pitches. A score function which combines spectral

and temporal features is proposed in order to select the optimal

pitch set. Note smoothing is also applied in a postprocessing

stage, employingHMMs and conditional randomfields (CRFs)

[13]. To the best knowledge of the authors, CRFs have not

been used in the past for transcription approaches. The system

was trained on a set of piano chords from the MAPS dataset

[6], and tested on classic, jazz, and random piano chords from

the same set, as well as on recordings from the RWC database

[14], Disklavier recordings prepared in [7], and the MIREX

recording used for the multiple-F0 estimation task [15]. The

proposed system is compared with several approaches in

the literature, where competitive results are provided using

several error metrics which indicate that the current system

outperforms state-of-the-art methods in many cases.

The outline of the paper is as follows. Section II describes

the preprocessing steps used in the transcription system.

The proposed multiple-F0 estimation method is presented in

Section III. The HMM- or CRF-based postprocessing steps

of the system are detailed in Section IV. In Section V,

the datasets used for training and testing are presented, the

employed error metrics are defined, and experimental results

are shown and discussed. Finally, conclusions are drawn and

future directions are indicated in Section VI, while in the

Appendices a derivation for the noise suppression algorithm

is given and the proposed log-frequency spectral envelope

estimation method is described.

II. PREPROCESSING

A. Resonator Time-Frequency Image

Firstly, the input music signal is loudness-normalized to

70dB relative to the reference amplitude for 16-bit audio

files, as in [16]. The resonator time-frequency image (RTFI)

is employed as a time-frequency representation [10]. The

RTFI selects a first-order complex resonator filter bank to

implement a frequency-dependent time-frequency analysis. It

can be formulated as:

RTFI(t,ω) = x(t) ∗ IR(t,ω)

(1)

where

IR(t,ω) = r(ω)e(−r(ω)+jω)t.

(2)

x(t) stands for the input signal, IR(t,ω) is the impulse

response of the first-order complex resonator filter with oscilla-

tion frequency ω and r(ω) is a decay factor which additionally

sets the frequency resolution.

Here, a constant-Q RTFI is selected for the time-frequency

analysis, due to its suitability for music signal processing

techniques, because the inter-harmonic spacings are the same

for any periodic sounds. The time interval between two

successive frames is set to 40ms, which is typical for multiple-

F0 estimation approaches [3]. A sampling rate of 44.1 kHz is

considered for the input samples (some recordings with sam-

pling rate 8 kHz which are presented in subsection V-A were

up-converted)and the centre frequency difference between two

neighboring filters is set to 10 cents (thus, the number of

bins per octave b is set to 120). The frequency range is set

from 27.5 Hz (A0) to 12.5 kHz (which reaches up to the 3rd

harmonic of C8). The employed absolute value of the RTFI

will be denoted as X[n,k] from now on, where n denotes the

time frame and k the log-frequency bin. When needed, X[k]

will stand for the RTFI slice for a single time-frame.

B. Spectral Whitening

Spectral whitening (or flattening) is a key preprocessing

step applied in multiple-F0 estimation systems, in order to

suppress timbral information and make the following analysis

more robust to different sound sources. When viewed from an

auditory perspective, it can be interpreted as the normalization

of the hair cell activity level [17]. In this paper, we employ

a method similar to the one in [3], but modified for log-

frequency spectra instead of linear frequency ones. For each

frequency bin, the power within a subband of1

multiplied by a Hann-window Whann[k] is computed. The

square root of the power within each subband is:

3octave span

σ[k] =

?1

K

k+K/2

?

l=k−K/2

Whann[l]|X[l]|2

?1/2

(3)

where K = b/3 = 40 bins. Afterwards, each bin is scaled

according to:

Y [k] = (σ[k])ν−1X[k]

(4)

where ν is a parameter which determines the amount of

spectral whitening applied and X[k] is the absolute value of

the RTFI for a single time frame, and Y [k] is the final whitened

RTFI slice. As in [3], ν was set to 0.33.

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BENETOS AND DIXON: JOINT MULTI-PITCH DETECTION USING HARMONIC ENVELOPE ESTIMATION FOR POLYPHONIC MUSIC TRANSCRIPTION3

AUDIO

LOUDNESS

NORMALIZATION

RTFI

ANALYSIS

SPECTRAL

WHITENING

NOISE

SUPPRESSION

PREPROCESSING

SALIENCE

FUNCTION

PITCH CANDIDATE

SELECTION

MULTIPLE-F0 ESTIMATION

PITCH SET

SCORE FUNCTION

FOR EACH C ⊆ C

POSTPROCESSING

TRANSCRIPTION

C

Fig. 1. Diagram for the proposed automatic transcription system.

C. Noise Suppression

In [9], an algorithm for noise level estimation was proposed,

based on the assumption that noise peaks are generated from a

white Gaussian process, and the resulting spectral amplitudes

obey a Rayleigh distribution. Here, an approach based on

pink noise assumption (elsewhere called 1/f noise or equal-

loudness noise) is proposed. In pink noise, each octave carries

an equal amount of energy, which corresponds well to the

approximately logarithmic frequency scale of human auditory

perception. Additionally, it occurs widely in nature, contrary

to white noise and is also suitable for the employed time-

frequency representation used in this work. Initial experiments

were performed using a pink noise generator and the MAT-

LAB distribution fitting toolbox. It was shown that when fitting

the pink noise amplitudes with the exponential probability

distribution, the resulting log likelihood was -286, compared

to -539 for the Rayleigh distribution, thus motivating for the

exponential distribution assumption.

The proposed signal-dependent noise estimation algorithm

is as follows:

1) Perform a two-stage median filtering procedure on Y [k],

in a similar way to [18]. The span of the filter is set to

1

3octave. The resulting noise representation N[k] gives a

rough estimate of the noise level.

2) Using the noise estimate, a transformation from the log-

frequency spectral coefficients to cepstral coefficients is

performed [19]:

cξ=

K′

?

k=1

log(N[k])cos

?

ξ

?

k −1

2

?π

K′

?

(5)

where K′= 1043 is the total number of log-frequency bins

in the RTFI and Ξ is the number of cepstral coefficients

employed, ξ = 0,...,Ξ − 1.

3) A smooth curve in the log-magnitude, log-frequency do-

main is reconstructed from the first D cepstral coefficients:

log|Nc(¯ ω)| ≈ exp

?

c0+ 2

D−1

?

ξ=1

cξ· cos(ξ¯ ω)

?

(6)

4) The resulting smooth curve is mapped from ¯ ω into k.

Assuming that the noise amplitude follows an exponential

distribution, the expected value of the noise log amplitudes

E{log(|Nc(¯ ω)|)} is equal to log(λ−1) − γ, where γ is

the Euler constant (≈ 0.5772). Since the mean of an

exponential distribution is equal to

the linear amplitude scale can be described as:

1

λ, the noise level in

LN(¯ ω) = Nc(¯ ω) · eγ

(7)

The analytic derivation of E{log(|Nc(¯ ω)|)} can be found

in Appendix A.

In this work, the number of cepstral coefficients used was set to

D = 50. Let Z[k] stand for the whitened and noise-suppressed

RTFI representation.

III. MULTIPLE-F0 ESTIMATION

In this section, multiple-F0 estimation, being the core of

the proposed transcription system, is described. Performed on

a frame-by-frame basis, a pitch salience function is generated,

tuning and inharmonicity parameters are extracted, candidate

pitches are selected, and for each possible pitch combination

an overlapping partial treatment is performed and a score

function is computed. In Fig. 1, the diagram for the proposed

automatic transcription system is depicted, where the various

stages for multiple-F0 estimation can be seen.

A. Salience Function Generation

In the linear frequency domain, considering a pitch p

of a musical instrument sound with fundamental frequency

fp,1 and inharmonicity coefficient βp, partials are located at

frequencies:

fp,h= hfp,1

?

1 + (h2− 1)βp

(8)

where h ≥ 1 is the partial index [3]. Inharmonicity occurs

due to string stiffness, where all partials of an inharmonic

instrument have a frequency that is higher than their expected

harmonic value [20]. Consequently in the log-frequency do-

main, considering a pitch p at bin kp,0, overtones are located

at bins:

?

where b = 120 refers to the number of bins per octave.

kp,h= kp,0+b · log2(h) +b

2log2

?

1 + (h2− 1)βp

??

(9)

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4IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING

In addition, variations occur concerning the position of the

fundamental; in [21], a model is proposed assuming that the

frequency of the first partial can be shifted by a specific

tuning factor. In this work, a pitch salience function s[p,δp,βp]

operating in the log-frequency domain is proposed, which

incorporates tuning and inharmonicity information:

s[p,δp,βp] =

H

?

h=1

max

mh

?

J[kp,h+ δp,mh,βp]

?

(10)

where

J[k,mh,βp] =

?

Z

?

k +

?

bmh+b

2log2(1 + (h2− 1)βp)

??

(11)

δp is the tuning deviation, and mh ∈ N∗specifies a search

range around overtone positions, belonging to the interval

(ml

⌈(M−1) log2(h)+log2(h+1)

M

⌋. M ∈ R∗

the width of the interval, since in the log-frequency domain

the search space for each harmonic is inversely proportional

to the harmonic index. Here M was set to 60, so the search

range for the 2nd harmonic is [−2,2] log-frequency bins, and

for the 3rd and 4th harmonics is [−1,1] bins.

While the employed salience functions in the linear fre-

quency domain (e.g. [18]) used a constant search space for

each overtone, the proposed log-frequency salience function

sets the search space to be inversely proportional to the

partial index. The number of considered overtones H is set

to 13 at maximum. The tuning deviation δptakes values from

[−4,...,4] log-frequency bins for each pitch (thus having

a tuning search space of ±40 cents around the reference

tuning frequency), thus allowing the detection of notes that

are not tuned using the reference frequency. The range of

the inharmonicity coefficient βpis set between 0 (completely

harmonic sounds) and 5·10−4(moderately inharmonic sounds,

e.g. from a baby grand piano [20]). The explicit modelling of

inharmonicity can also be useful for temperament estimation

systems, such as [22].

In order to accurately estimate the ideal tuning factor and the

inharmonicity coefficient for each pitch, a 2-D maximization

procedure is applied to s[p,δp,βp] for each pitch p, in a

similar manner to the work in [6]. Here p = 1,...,88 which

corresponds to notes A0 to C8, where the pitch reference

is A4 (MIDI note 69) = 440 Hz. This results in a pitch

salience function estimate s′[p], a tuning deviation vector and

an inharmonicity coefficient vector. All in all, the compu-

tational complexity for the salience function generation is

O(Np·Nh·Nδ·Nβ), where Np= 88, Nh= 13, Nδ= 9, and

Nβ= 6 (the number of discrete values each variable takes).

Using the information extracted from the tuning and in-

harmonicity estimation, a harmonic partial sequence (HPS)

V [p,h], which contains magnitude information from X[k] for

each harmonic of each candidate pitch, is also stored for

further processing. For example, V [39,2] corresponds to the

magnitude of the 2nd harmonic of p = 39 (which is note B3).

An example of the salience function generation is given in Fig.

2, where the RTFI spectrum of an isolated F♯3 note played by

h,mu

h), where ml

h= ⌈log2(h−1)+(M−1) log2(h)

+is a factor controlling

M

⌋, mu

h=

k

(a)

p

(b)

0

10

2030

40

50

6070

8090

0

200

400

600

8001000

1200

0

2

4

6

0

1

2

3

Fig. 2. (a) The RTFI slice X[k] of an F♯3 piano sound. (b) The corresponding

pitch salience function s′[p].

a piano is seen, along with its corresponding salience s′[p].

The highest peak in s′[p] corresponds to p = 34, thus F♯3.

B. Pitch Candidate Selection

A set of conservative rules examining the harmonic partial

sequence structure of each pitch candidate is applied, which is

inspired by work from [1], [23]. These rules aim to reduce the

pitch candidate set for computational speed purposes. As can

be seen from Fig. 2, false peaks that correspond to multiples

and sub-multiples of the actual pitches occur in s′[p]. Here,

peaks in s′[p] that occur at sub-multiples of the actual F0s are

subsequently deleted. In the semitone space, these peaks occur

at −{12,19,24,28,...} semitones from the actual pitch.

A first rule for suppressing salience function peaks is setting

a minimum number for partial detection in V [p,h], similar to

[1]. At least three partials out of the first six need to be present

in the harmonic partial sequence (since there may be a missing

fundamental). A second rule discards pitch candidates with a

salience value less than 0.1 · max(s′[p]), as in [23].

Finally, after spurious peaks in s′[p] have been eliminated,

CN = 10 candidate pitches are selected from the highest

amplitudes of s′[p] [6]. The set of selected pitch candidates

will be denoted as C. Thus, the maximum number of possible

pitch candidate combinations that will be considered is 210,

compared to 288if the aforementioned procedures were not

employed. It should be stressed that this procedure does not

affect the transcription performance of the system, as tested

with the training set of piano chords described in subsection

V-A.

C. Overlapping Partial Treatment

Current approaches in the literature rely on certain as-

sumptions in order to recover the amplitude of overlapped

harmonics. In [24], it is assumed that harmonic amplitudes de-

cay smoothly over frequency (spectral smoothness). Thus, the

amplitude of an overlapped harmonic can be estimated from

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BENETOS AND DIXON: JOINT MULTI-PITCH DETECTION USING HARMONIC ENVELOPE ESTIMATION FOR POLYPHONIC MUSIC TRANSCRIPTION5

the amplitudes of neighboring non-overlapped harmonics. In

[25], the amplitude of the overlapped harmonic is estimated

through non-linearinterpolation on the neighboringharmonics.

In [26], each set of harmonics is filtered from the spectrum

and in the case of overlapping harmonics, linear interpolation

is employed.

In this work, an overlapping partial treatment procedure

based on spectral envelope estimation of candidate pitches

is proposed. The proposed spectral envelope estimation algo-

rithm for the log-frequency domain is presented in Appendix

B. For each possible pitch combination C ⊆ C, overlapping

partial treatment is performed, in order to accurately estimate

the partial amplitudes. The proposed overlapping partial treat-

ment procedure is as follows:

1) Given a set C of pitch candidates, estimate a partial

collision list.

2) For a given harmonic partial sequence, if the number of

overlapped partials is less than Nover, then estimate the

harmonic envelope SEp[k] of the candidate pitch using

only amplitude information from non-overlappedpartials.

3) For a given harmonic partial sequence, if the number

of overlapped partials is equal or greater than Nover,

estimate the harmonic envelope using information from

the complete harmonic partial sequence.

4) For each overlapped partial, estimate its amplitude using

the harmonic envelope parameters of the corresponding

pitch candidate (see Appendix B).

The output of the overlapping partial treatment procedure is

the updated harmonic partial sequence V [p,h] for each pitch

set combination.

D. Pitch set score function

Having selected a set of possible pitch candidates and

performed overlapping partial treatment on each possible com-

bination, the goal is to select the optimal pitch combination for

a specific time frame. In [9], Yeh proposed a score function

which combined four criteria for each pitch: harmonicity,

bandwidth, spectral centroid, and synchronicity. Also, in [23],

a simple score function was proposed for pitch set selection,

based on the smoothness of the pitch set. Finally, in [6] a

multipitch detection function was proposed, which employed

the spectral flatness of pitch candidates along with the spectral

flatness of the noise residual.

Here, a weighted pitch set score function is proposed, which

combines spectral and temporal characteristics of the candidate

F0s, and also attempts to minimize the noise residual to

avoid any missed detections. Also, features which concern

harmonically-related F0s are included in the score function,

in order to suppress any harmonic errors. Given a candidate

pitch set C ⊆ C with size |C|, the proposed pitch set score

function is:

|C|

?

where Lpis the score function for each candidate pitch p ∈ C,

and Lresis the score for the residual spectrum. Lpand Lres

L(C) =

p=1

(Lp) + Lres

(12)

are defined as:

Lp= w1Fl[p] + w2Sm[p] − w3SC[p] + w4PR[p] − w5AM[p]

Lres= w6Fl[Res]

(13)

Fl[p] denotes the spectral flatness of the harmonic partial

sequence:

Fl[p] =e[?H

1

H

The spectral flatness is a measure of the ‘whiteness’ of the

spectrum. Its values lie between 0 and 1 and it is maximized

when the input sequence is smooth, which is the ideal case for

an HPS. It has been used previously for multiple-F0 estimation

in [6], [23]. Here, the definition given for the spectral flatness

measure is the one adapted by the MPEG-7 framework, which

can be seen in [27].

Sm[p] is the smoothness measure of a harmonic partial

sequence, which was proposed in [23]. The definition of

smoothness stems from the spectral smoothness principle and

its definition stems from the definition of sharpness:

h=1log(V [p,h])]/H

?H

h=1V [p,h]

(14)

Sr[p] =

H

?

h=1

(SEp[kp,h] − V [p,h])

(15)

Here, instead of a low-pass filtered HPS using a Gaussian win-

dow as in [23], the estimated harmonic envelope SEpof each

candidate pitch is employed for the smoothness computation.

Sr[p] is normalized into¯ Sr[p] and the smoothness measure

Sm[p] is defined as: Sm[p] = 1 −¯ Sr[p]. A high value of

Sm[p] indicates a smooth HPS.

SC[p] is the spectral centroid for a given HPS and has been

used for the score function in [9]:

SC[p] =

?

?

?

?2 ·

?H

h=1h · |V [p,h]|2

?H

h=1|V [p,h]|2

(16)

It indicates the center of gravity of an HPS; for pitched

percussive instruments it is positioned at lower partials. A

typical value for a piano note would be 1.5 denoting that

the center of gravity of its HPS is between the 1st and 2nd

harmonic.

PR[p] is a novel feature, which stands for the harmonically-

related pitch ratio. Here, harmonically-related pitches [9] are

candidate pitches in C that have a semitone difference of

⌈12 · log2(l)⌋ = {12,19,24,28,...}, where l > 1,l ∈ N.

PR[p] is applied only in cases of harmonically-related pitches,

in an attempt to estimate the ratio of the energy of the

smoothed partials of the higher pitch compared to the energy

of the smoothed partials of the lower pitch. It is formulated

as follows:

PRl[p] =

3

?

h=1

V [p + ⌈12 · log2(l)⌋,h]

V [p,l · h]

(17)

where p stands for the lower pitch and p+⌈12·log2(l)⌋ for the

higher harmonically-relatedpitch. l stands for the harmonic re-

lation between the two pitches (fhigh= lflow). In case of more

than one harmonic relation between the candidate pitches,

a mean value is computed: PR[p] =

1

|Nhr|

?

l∈NhrPRl[p],

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6IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING

where Nhris the set of harmonic relations. A high value of PR

indicates the presence of a pitch in the higher harmonically-

related position.

Another novel feature applied in the case of harmonically-

related F0s, measuring the amplitude modulation similarity

between an overlapped partial and a non-overlapped partial

frequency region, is proposed. The feature is based on the

common amplitude modulation (CAM) assumption, which

states that partial amplitudes of a harmonic source are cor-

related over time [28]. Here, an extra assumption is made

that frequency deviations are also correlated over time. The

time-frequency region of a non-overlapped partial is compared

with the time-frequency region of the fundamental. In order

to compare 2-D time-frequency partial regions, the normalized

tensor scalar product [29] is used:

AMl[p] =

3

?

h=1

?

i,jΛijBh

??

ij

??

i,jΛijBh

ij·

i,jΛijBh

ij

(18)

where

Λ=

=

X[n0: n1,kp,1− 4 : kp,1+ 4]

X[n0: n1,kp,hl− 4 : kp,hl+ 4]Bh

(19)

where i,j denote the indexes of matrices Λ and Bhand n0and

n1= n0+ 5 denote the frame boundaries of the time-frame

region selected for consideration. The normalized tensor scalar

product is a generalization of the cosine similarity measure,

which compares two vectors, finding the cosine of the angle

between them.

Res denotes the residual spectrum, which can be expressed

in a similar way to the linear frequency version in [6]:

Res =

?

Z[k]

?

∀p,∀h,

????k − kp,h>∆W

2

????

?

(20)

where ∆W denotes the mainlobe width of the employed

window W. In order to find a measure of the ‘whiteness’ of the

residual, 1−Fl[Res], which denotes the residual smoothness,

is used.

It should be noted that features Fl,Sr,SC,PR,AM have

also been weighted by the salience function of the candidate

pitch and divided by the sum of the salience function of the

candidate pitch set, for normalization purposes. In order to

train the weight parameters wi,i = 1,...,6 of the features in

(13), we used the Nelder-Mead search algorithm for parameter

estimation [30]. The training set employed for experiments is

described in subsection V-A. Finally, the pitch candidate set

that maximizes the score function:

ˆC = argmax

C⊆C

L(C)

(21)

is selected as the pitch estimate for the current frame.

IV. POSTPROCESSING

Although temporal information has been included in the

frame-based multiple-F0 estimation system, additional post-

processing is needed in order to track notes over time, and

eliminate any single-frame errors. In the transcription litera-

ture, hidden Markov models (HMMs) [31] have been used

(a)

MIDI Scale

(b)

MIDI Scale

200

400600

800

1000 1200 1400 1600 1800 20002200

200

400600

800

1000 1200 1400 1600 18002000 2200

50

60

70

50

60

70

Fig. 3.

(jazz piano) in a 10 ms time scale (a) Output of the multiple-F0 estimation

system (b) Piano-roll transcription after HMM postprocessing.

Transcription output of an excerpt of ‘RWC MDB-J-2001 No. 2’

for postprocessing. In [32], three-state note-event HMMs were

trained for each pitch, where the input features were the

pitch salience value and the onset strength of the current

frame. Poliner and Ellis [7] trained two-state HMMs for each

note using MIDI data from the RWC database and used as

observation probabilities the pseudo-posteriors of the one-

versus-all SVM classifiers used for frame-based multiple-F0

estimation of piano recordings. In [33], each possible note

combination between two onsets is represented by one HMM

state, where the state transitions were also learned using MIDI

data and the observation probability is given by the spectral

flatness of the HPS of the pitch set. Finally, Ca˜ nadas-Quesada

et al. also utilized two-state HMMs for each pitch that were

trained using MIDI data, where the observation likelihood is

given by the salience of the candidate pitch [8]. In all cases

mentioned, the Viterbi algorithm is used to extract the best

state sequence.

In this work, two postprocessing methods were employed:

the first using HMMs and the second using conditional random

fields (CRFs), which to the authors’ knowledge have not been

used before in music transcription research.

A. HMM Postprocessing

In this work, each pitch p = 1,...,88 is modeled by a

two-state HMM, denoting pitch activity/inactivity, as in [7],

[8]. The observation sequence is given by the output of the

frame-based multiple-F0 estimation step for each pitch p:

Op= {op[n]}, n = 1,...,N, while the state sequence is given

by Qp = {qp[n]}. Essentially, in the HMM post-processing

step, detected pitches from the multiple-F0 estimation step

are tracked over time and their note activation boundaries

are estimated using information from the salience function.

In order to estimate the state priors P(qp[1]) and the state

transition matrix P(qp[n]|qp[n − 1]), MIDI files from the

RWC database [14] from the classic and jazz subgenres were

employed, as in [8]. For each pitch, the most likely state

sequence is given by:

Q′

p= argmax

qp[n]

?

n

P(qp[n]|qp[n − 1])P(op[n]|qp[n])

(22)

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BENETOS AND DIXON: JOINT MULTI-PITCH DETECTION USING HARMONIC ENVELOPE ESTIMATION FOR POLYPHONIC MUSIC TRANSCRIPTION7

in

P(op[n]|qp[n]), we employ a sigmoid curve which has

as input the salience function of an active pitch from the

output of the multiple-F0 estimation step:

ordertoestimatetheobservationprobabilities

P(op[n]|qp[n] = 1) =

1

1 + e−(s′[p,n]−1)

(23)

where s[p,n] denotes the salience function value at frame

n. The output of the HMM-based postprocessing step is

generated using the Viterbi algorithm. The transcription output

of an example recording at the multiple-F0 estimation stage

and after the HMM postprocessing is depicted in Fig. 3. In

addition, in Fig. 4(a) the graphical structure of the employed

HMMs is displayed.

B. CRF Postprocessing

Although the HMMs have repeatedly proved to be an

invaluable tool for smoothing sequential data, they suffer from

the limitation that the observation at a given time frame

depends only on the current state. In addition, the current state

depends only on its immediate predecessor. In order to allevi-

ate these assumptions, conditional random fields (CRFs) [13]

can be employed. CRFs are undirected graphical models that

directly model the conditional distribution P(Q|O) instead of

the joint probability distribution P(Q,O) as in the HMMs.

This indicates that HMMs belong to the class of generative

models, while the un-directed CRFs are discriminative models.

The assumptions concerning the state independence and the

observation dependence on the current state which are posed

for the HMMs are relaxed.

In this work, 88 linear-chain CRFs are employed (one for

each pitch p), where the current state q[n] is dependent not

only on the current observation o[n], but also on o[n−1]. For

learning, we used the same note priors and state transitions

from the RWC database which were also utilized for the

HMMs post-processing. For inference, the most likely state

sequence for each pitch is computed using a Viterbi-like

recursion which estimates:

Q′

p= argmax

Qp

P(Qp|Op)

(24)

where P(Qp|Op) =

probability for a given state is given as a sum of two potential

functionsTS:

1

1 + e−(s′[p,n]−1)+

?

nP(qp[n]|Op) and the observation

P(Op|qp[n] = 1) =

1

1 + e−(s′[p,n−1]−1)

(25)

It should be noted that in our employed CRF model we assume

that each note state depends only on its immediate predecessor

(like in the HMMs), while the relaxed assumption over the

HMMs concerns the observation potentials. The graphical

structure of the linear-chain CRF which was used in our

experiments is presented in Fig. 4(b).

V. EVALUATION

A. Datasets

For training the system parameters, samples from the MIDI

Aligned Piano Sounds (MAPS) database [6] were used. The

qp[1]qp[2]qp[3]

op[1]

op[2]

op[3]

...

(a)

qp[1]qp[2]qp[3]

op[1]

op[2]

op[3]

...

(b)

Fig. 4.

networks for postprocessing.

Graphical structure of the employed (a) HMM (b) Linear chain CRF

MAPS database contains real and synthesized recordings of

isolated notes, musical chords, random chords, and music

pieces, produced by 9 real and synthesized pianos in different

recording conditions, containing around 10000 sounds in total.

Recordings are stereo, sampled at 44.1 kHz, while MIDI files

are provided as ground truth. Here, 103 samples from two

piano types were employed for training1, while 6832 samples

from the remaining 7 piano types were used for testing on

polyphonic piano sounds. The test set consists of classic, jazz,

and randomly generated chords of polyphony levels 1-6, while

the note range was C2-B6, in order to match the experiments

performed in [6]. It should be noted that the postprocessing

stage was not employed for the MAPS dataset, since it consists

of isolated chords.

For the transcription experiments, we firstly used 12 ex-

cerpts from the RWC database [14], which have been used in

the past to evaluate polyphonic music transcription approaches

in [8], [34], [35]. A list of the employed recordings along

with the instruments present in each one is shown in the top

half of Table I. The recordings containing ‘MDB-J’ in their

RWC ID belong to the jazz genre, while those that contain

‘MDB-C’ belong to the classic genre. For the recording titles

and composer, the reader can refer to [35]. Five additional

pieces were also selected from the RWC database, which

have not yet been evaluated in the literature. These pieces are

described in the bottom half of Table I (data 13-17). Also,

the full wind quintet recording from the MIREX multi-F0

development set was also used for experiments [15]. Finally,

the test dataset developed by Poliner and Ellis [7] was also

used for transcription experiments. It contains 10 one-minute

recordings from a Yamaha Disklavier grand piano, sampled at

8 kHz.

As far as ground-truth for the RWC data 1-12 Table I,

non-aligned MIDI files are provided along with the origi-

1Trained weight parameters wiwere {1.3,1.4,0.6,0.5,0.2,25}.

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8IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING

RWC ID Instruments

Piano

Piano

Guitar

Guitar

Guitar

Guitar

Piano

Piano

Flute + Piano

Flute + String Quartet

Cello + Piano

Tenor + Piano

String Quartet

Clarinet + String Quartet

Harpsichord

Violin (polyphonic)

Violin

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

RWC-MDB-J-2001 No. 1

RWC-MDB-J-2001 No. 2

RWC-MDB-J-2001 No. 6

RWC-MDB-J-2001 No. 7

RWC-MDB-J-2001 No. 8

RWC-MDB-J-2001 No. 9

RWC-MDB-C-2001 No. 30

RWC-MDB-C-2001 No. 35

RWC-MDB-J-2001 No. 12

RWC-MDB-C-2001 No. 12

RWC-MDB-C-2001 No. 42

RWC-MDB-C-2001 No. 49

RWC-MDB-C-2001 No. 13

RWC-MDB-C-2001 No. 16

RWC-MDB-C-2001 No. 24a

RWC-MDB-C-2001 No. 36

RWC-MDB-C-2001 No. 38

TABLE I

THE RWC DATA USED FOR TRANSCRIPTION EXPERIMENTS.

nal 44.1 kHz recordings. However, these MIDI files contain

several note errors and omissions, as well as unrealistic

note durations, thus making them unsuitable for transcription

evaluation. As in [8], [34], [35], aligned ground-truth MIDI

data was created for the first 23s of each recording, using

Sonic Visualiser [36] for spectrogram visualization and MIDI

editing. For the RWC data 13-17 in Table I, the newly-released

syncRWC ground truth annotations were utilized2.

B. Figures of Merit

In order to assess and compare the performance of the

proposed system, several figures of merit from the automatic

transcription literature are employed. For the piano chords

using the MAPS dataset, the precision, recall, and F-measure

are used:

tp

tp + fp, Rec =

where tp is the number of correctly estimated pitches, fp is

the number of false pitch detections, and fn is the number of

missed pitches.

For the recordings used for the transcription experiments,

several metrics are employed. It should be noted that all

evaluations take place by comparing the transcribed output

and the ground-truth MIDI files at a 10 ms scale, as is the

standard for the multiple-F0 MIREX evaluation [15]. The first

metric that is used is the overall accuracy, defined by Dixon

[37]:

Acc1=

fp + fn + tp

When Acc1= 1, a perfect transcription is achieved [7]. For

(27), tp,fp, and fn refer to the number of true positives, false

positives, and false negatives respectively, for all frames of the

recording.

A second accuracy measure is also used, which was pro-

posed by Kameoka et al. [34] which also includes pitch substi-

tution errors. Let Nref[n] stand for the number of ground-truth

Pre =

tp

tp + fn, F =2 · Pre · Rec

Pre + Rec

(26)

tp

(27)

2http://staff.aist.go.jp/m.goto/RWC-MDB/AIST-Annotation/SyncRWC/

pitches at frame n, Nsys[n] the number of detected pitches, and

Ncorr[n] the number of correctly detected pitches. The number

of false negatives at the current frame is Nfn[n], the number of

false positives is Nfp[n], and the number of substitution errors

is given by Nsubs[n] = min(Nfn[n],Nfp[n]). The accuracy

measure is defined as:

Acc2=

?

nNref[n] − Nfn[n] − Nfp[n] + Nsubs[n]

?

From the aforementioned definitions, several error metrics

have been defined in [7] that measure the substitution errors

(Esubs), miss detection errors (Efn), false alarm errors (Efp),

and the total error (Etot):

nNref[n]

(28)

Esubs

=

?

?

?

Esubs+ Efn+ Efp

nmin(Nref[n],Nsys[n]) − Ncorr[n]

?

?

?

nNref[n]

Efn

=

nmax(0,Nref[n] − Nsys[n])

nNref[n]

nmax(0,Nsys[n] − Nref[n])

nNref[n]

Efp

=

Etot

=

(29)

It should be noted that the aforementioned error metrics can

exceed 100% if the number of false alarms is very high [7].

C. Results

1) MAPS Database: For the isolated chord experiments

using the MAPS database, the performance of the proposed

transcription system compared with the results shown in

[11] and [6] is shown in Fig. 5, organized according to

the polyphony level of the ground truth (experiments were

performed with unknown polyphony). The mean F-measures

for polyphony levels L = 1,...,6 are 91.86%, 88.61%,

91.30%, 88.83%, 88.14%, and 69.55% respectively. It should

be noted that the subset of polyphony level 6 consists only

of 350 samples of random notes and not of classical and

jazz chords. As far as precision is concerned, reported rates

are high for all polyphony levels, ranging from 89.88% to

96.19%, with the lowest precision rate reported for L = 1.

Recall displays the opposite performance, reaching 96.40% for

one-note polyphony, and decreasing with the polyphony level,

reaching 86.53%, 88.65%, 85.00%, and 83.14%, and 57.44%

for levels 2-6.

In terms of a general comparison between all systems, the

global F-measure for all sounds was used, where the proposed

system outperforms all other approaches, reaching 88.54%.

The system in [11] reports 87.47%, the system in [6] 83.70%,

and finally the algorithm of [24] used for comparison in [6]

reports 85.25%. By applying the same significance tests as in

[11], it can be seen that the proposed method outperforms the

methods of [6], [11], [24] in a statistically significant manner

with 95% confidence. The aforementioned methods used for

comparison follow the same pattern when Pre and Rec are

concerned, reporting high Pre rates for all polyphony levels

and decreasing Rec rates as polyphony increases.

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BENETOS AND DIXON: JOINT MULTI-PITCH DETECTION USING HARMONIC ENVELOPE ESTIMATION FOR POLYPHONIC MUSIC TRANSCRIPTION9

L

%

Proposed

[11][6][24]

1

2

34

5

6

0

10

20

30

40

50

60

70

80

90

100

Fig. 5. Multiple-F0 estimation results for the MAPS database (in F-measure)

with unknown polyphony, organized according to the ground truth polyphony

level L.

2) RWC + MIREX Database: Transcription results using

the RWC recordings 1-12 for the proposed system with CRF

postprocessing can be found in Table II. A comparison is

made using several reported results in the literature for the

same files [8], [34], [35], where the proposed method reports

improved mean Acc2. Additional results were also produced

for this paper using a previous method [12] submitted by the

authors for the MIREX 2010 evaluation, which has a similar

front-end but performs multiple-F0 estimation in an iterative

fashion. Additional comparative results which demonstrate

lower accuracy rates compared to the proposed system can

be found in [8], that are omitted here for brevity. It should

be noted that the proposed system demonstrates impressive

results for some recordings compared to the state-of-the-art

(e.g. in file 11, which is a cello-piano duet) while in some

cases it falls behind. In file 4 for example, results are inferior

compared to state-of-the-art, which could be attributed to the

digital effects applied in the recording (the present system was

created mostly for transcribing classical and jazz music). As

far as the standard deviation of the Acc2metric is concerned,

the proposed system reports 11.5% which is comparable to

the approaches in Table II, although it is worth noting that the

lowest standard deviation is reported for the method in [12].

For the RWC recordings 13-17 and the MIREX recording,

transcription results can be found in Table III. It should be

noted that no results have been published in the literature for

these recordings. In general, it can be seen that bowed string

transcriptions are more accurate than woodwind transcriptions.

Concerning the statistical significance of the proposed

method’s performance for the RWC recordings 1-12 compared

to the various methods shown in Table II, the recognizer

comparison technique described in [38] was employed. The

number of pitch estimation errors of the two methods in

comparison is assumed to be distributed according to the

binomial law. The error rate of the proposed method is

ˆ ǫ1= Etot= 0.395, while the error rate for the methods of [8],

[12], [34], [35] is ˆ ǫ2= 0.488, ˆ ǫ3= 0.409, ˆ ǫ4= 0.438, and

ˆ ǫ5= 0.404, respectively. The number of examples used to gen-

erate these error rates is ζ = 12·23·100 = 27600. Considering

95% confidence, it can be seen that ˆ ǫi− ˆ ǫ1 ≥ z0.05

where i = 2,...,5, ˆ ǫ =ˆ ǫ1+ˆ ǫi

2

, and z0.05= 1.65 which can be

determined from tables of the Normal law. This demonstrates

that the performance of the proposed transcription system

is significantly better when compared with the methods in

[8], [12], [34], [35]. It should be noted however that the

significance threshold was only just surpassed when compared

with the method of [34].

Additional insight to the proposed system’s performance

for all 17 RWC recordings and the MIREX one is given

in Table IV, where the error metrics of subsection V-B are

presented using different postprocessing configurations. It can

be seen that without any postprocessing Acc2= 53.8%, while

when using the HMMs an improvement of 4.6% is reported

and when the CRFs are employed, the improvement is 5.7%.

It can also be seen that the note postprocessing procedures

mainly decrease the number of false alarms (as can be seen

in Efp), at the expense however of missed detections (Efn).

Especially for the HMM postprocessing, a large number of

missed detections have impaired the system’s performance. It

should be also noted that the accuracy improvement of the

CRF postprocessing step over the HMM one is statistically

significant with 95% confidence, using the technique in [38].

Specifically, the number of examples used to generate the error

rates is ζ = 42200, the error rate for the CRF postprocessing

step is ˆ ǫCRF = 0.405, for the HMM step is ˆ ǫHMM = 0.416,

and the significance threshold for this experiment was found

to be 0.72% in terms of the error rate, which is surpassed by

the CRF postprocessing (being 1.1%).

In order to test the contribution of each feature in the pitch

set score function (13) to the performance of the transcription

system, experiments were made on RWC recordings 1-12.

For each experiment, the weight wi, i = 1,...,6 in the

score function that corresponds to each feature was set to

0. Results are shown in Table V, where it can clearly be

seen that the most crucial feature is Fl[Res], which is the

residual flatness. Without that feature, the score function might

select a single pitch candidate and produce several missed

detections. However, it can clearly be seen that each feature

significantly contributes to the final transcription result of

60.5%. When testing the contribution of the inharmonicity

estimation in the salience function, the same experiment took

place with no inharmonicity search, where Acc2 = 59.7%.

By employing the statistical significance test of [38], the

performance improvement when inharmonicity estimation is

enabled is significant with 90% confidence. It should be noted

however that the contribution of the inharmonicity estimation

procedure depends on the instrument sources that are present

in the signal. In addition, by disabling the overlapping partial

treatment procedure for the same experiment, it was shown

that Acc2= 38.0%, with Efp= 20.4%, which indicates that

false alarms from the overlapped peaks might be detected by

the system. The 22.5% difference in terms of accuracy for

the overlapping partial treatment is shown to be statistically

significant with 95% confidence, using the method in [38].

Concerning the performance of the proposed noise suppres-

sion algorithm, comparative experiments were performed us-

?2ˆ ǫ/ζ,

Page 10

10IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING

Proposed

60.2%

74.1%

50.0%

35.7%

75.0%

57.9%

66.8%

54.8%

74.4%

64.0%

58.9%

53.9%

60.5%

11.5%

[12]

58.1%

50.6%

42.8%

28.8%

63.9%

52.0%

51.5%

47.0%

54.9%

58.4%

46.2%

47.6%

51.2%

9.0%

[8][35]

59.0%

63.9%

51.3%

68.1%

67.0%

77.5%

57.0%

63.6%

44.9%

48.9%

37.0%

35.8%

56.2%

12.9%

[34]

64.2%

62.2%

63.8%

77.9%

75.2%

81.2%

70.9%

63.2%

43.2%

48.1%

37.6%

27.5%

59.6%

16.9%

1

2

3

4

5

6

7

8

9

63.5%

72.1%

58.6%

79.4%

55.6%

70.3%

49.3%

64.3%

50.6%

55.9%

51.1%

38.0%

59.1%

11.5%

10

11

12

Mean

Std.

TABLE II

TRANSCRIPTION RESULTS (Acc2) FOR THE RWC RECORDINGS 1-12

USING THE PROPOSED METHOD WITH CRF POSTPROCESSING, COMPARED

WITH OTHER APPROACHES.

Proposed

48.2%

41.8%

66.8%

70.7%

75.2%

41.3%

57.4%

15.3%

[12]

38.4%

41.2%

41.0%

57.0%

52.2%

39.9%

44.9%

7.7%

13

14

15

16

17

MIREX

Mean

Std.

TABLE III

TRANSCRIPTION RESULTS (Acc2) FOR RWC RECORDINGS 13-17 AND

THE MIREX RECORDING, USING THE PROPOSED METHOD WITH CRF

POSTPROCESSING, COMPARED WITH THE METHOD IN [12].

ing the 2-stage noise suppression procedure that was proposed

for multiple-F0 estimation in [18], using the RWC recordings

1-12. The noise suppression procedure of [18] consists of

median filtering on the whitened spectrum, followed by a

second median filtering which does not take into account

spectral peaks. Experiments with CRF postprocessing showed

that transcription accuracy using the 2-state noise suppression

algorithm was Acc2= 56.0%, compared to the 60.5% of the

proposed method. The performance difference is statistically

significant with 95% confidence, using the method of [38].

3) Disklavier dataset [7]: Transcription results using the 10

Disklavier recording test set created by Poliner and Ellis can

be found in Table VI, along with results from other approaches

reported in [7]. Also, additional results were produced by the

authors using our iterative MIREX-submitted method, which

has a similar preprocessing front-end and the same salience

function [12]. It can be seen that the best results are reported

for the method in [7] while the proposed system is second-

best, although it should be noted that the training set for the

method by Poliner and Ellis used data from the same source as

the test set. In addition, the method in [7] has displayed poor

generalization performance when tested on different datasets,

as can be seen from results shown in [7] and [8].

In Table VII, several error metrics are displayed for the

Disklavier dataset, using different postprocessing configura-

tions for the proposed method. The same pattern that was

shown for the RWC data is shown here, where using the

Method

No Post.

HMM Post.

CRF Post.

Acc1

54.4%

57.3%

58.9%

Acc2

53.8%

58.4%

59.5%

Etot

46.2%

41.6%

40.5%

Esubs

11.9%

5.4%

7.1%

Efn

19.4%

32.2%

25.3%

Efp

14.9%

4.0%

8.2%

TABLE IV

TRANSCRIPTION ERROR METRICS FOR THE PROPOSED METHOD USING

RWC RECORDINGS 1-17 AND THE MIREX RECORDING, USING

DIFFERENT POSTPROCESSING TECHNIQUES.

All

Fl Sm

59.2%

SC

58.6%

PR

53.5%

AM

59.4%

Fl[Res]

29.1%60.5%56.3%

TABLE V

TRANSCRIPTION RESULTS (Acc2) FOR THE RWC RECORDINGS 1-12

USING CRF POSTPROCESSING, WHEN FEATURES ARE REMOVED FROM

THE SCORE FUNCTION (13).

HMMs a small improvement of 0.4% is reported, while the

improvement for the CRFs is 2.6%. The difference in the

improvement over the RWC data can be attributed to the

faster tempo of the Disklavier pieces. It has been argued in [8]

that HMM note smoothing provides greater improvement for

music pieces with slow tempo. For the HMM postprocessing,

false alarms are again reduced at the expense of additional

missed detections, while the CRF postprocessing displays an

improvement over the missed detection errors, at the expense

of false alarms.

VI. CONCLUSIONS

In this work, a joint multiple-F0 estimation system for au-

tomatic transcription of polyphonic music was proposed. As a

front-end, the constant-Q resonator time-frequency image was

selected due to its suitability for music signal representation.

Contributions of the paper include:

• A noise suppression algorithm based on a pink noise

assumption

• A log-frequency salience function that supports tuning

and inharmonicity estimation

• Overlapping partial treatment procedure using harmonic

envelopes of pitch candidates

• A pitch set score function incorporating spectral and

temporal features

• An algorithm for log-frequency spectral envelope estima-

tion based on the discrete cepstrum

• Note smoothing using conditional random fields (CRFs)

The system was trained on a set of isolated piano chords

from the MAPS database and tested on recordings from the

RWC database, the Disklavier database from [7], and the

MIREX multipitch estimation recording [15]. Comparative

results are provided using various evaluation metrics over

several state-of-the-art methods, as well as on a method

previously developed by the authors. The proposed system

displays promising and robust results, surpassing state-of-

the-art performance in many cases, considering also the fact

that the training and testing datasets originate from different

sources. For the RWC recordings, the improvement by the

proposed system was found statistically significant compared

Page 11

BENETOS AND DIXON: JOINT MULTI-PITCH DETECTION USING HARMONIC ENVELOPE ESTIMATION FOR POLYPHONIC MUSIC TRANSCRIPTION11

Method

Acc1

Proposed

49.4%

[11]

43.3%

[7][32]

41.2%

[39]

38.4%56.5%

TABLE VI

MEAN TRANSCRIPTION RESULTS (Acc1) FOR THE RECORDINGS FROM [7]

USING CRF POSTPROCESSING, COMPARED WITH OTHER APPROACHES.

Method

No Post.

HMM Post.

CRF Post.

Acc1

46.8%

47.2%

49.4%

Acc2

48.2%

48.3%

49.8%

Etot

51.8%

51.7%

50.2%

Esubs

10.5%

8.5%

10.1%

Efn

35.2%

38.1%

31.4%

Efp

6.1%

5.1%

8.6%

TABLE VII

TRANSCRIPTION ERROR METRICS USING THE RECORDINGS FROM [7] AND

DIFFERENT POSTPROCESSING TECHNIQUES.

to other approaches in the literature. For public evaluation, an

iterative variant of this system was submitted for the MIREX

2010 multiple-F0 estimation task [12] displaying encouraging

results, even without any postprocessing. In general, the pro-

posed system showed improvement over the one in [12] that

can be attributed to the use of pitch combinations instead of

iterative selection, and the postprocessing module.

In the future, the present system will be submitted for the

next MIREX evaluation. In general, results generally indicated

a relatively low false alarm rate, but a considerable number

of missed detections. This can be rectified in the future

by relaxing several assumptions concerning the inharmonic-

ity range and spectral smoothness (which would also allow

for multipitch estimation of inharmonic instruments such as

marimba or vibraphone), but at the expense of additional false

positives. Also, in order to improve transcription performance,

training could be applied using a multi-instrument dataset,

such as the one used in [24]. In addition, more general forms

of CRFs that link multiple states together could improve note

prediction and smoothing. Finally, system performance can be

improved by performing joint multiple-F0 estimation and note

tracking, instead of frame-based multipitch estimation with

subsequent note tracking.

APPENDIX A

EXPECTED VALUE OF NOISE LOG-AMPLITUDES

We assume that the noise amplitude follows an exponential

distribution. In order to find the expected value of the noise log

amplitudes E{log(|Nc(¯ ω)|)}, we adopt a technique similar to

[9]. Let Θ = log(Nc(¯ ω)) = Φ(N):

?+∞

=

−∞

?+∞

= log(λ−1) − γ

E{Θ}=

−∞

?+∞

−γ − λlog(λ) ·

θp(θ)dθ =

?+∞

−∞

θp(Φ−1(θ))

????

dΦ−1(θ)

dθ

????

λθe−λeθeθdθ =

?+∞

e−λψdψ

0

λlog(ψ)e−λψdψ

=

0

(30)

where γ is the Euler constant:

γ = −

?+∞

0

e−ψlog(ψ)dψ ≈ 0.57721.

(31)

APPENDIX B

LOG-FREQUENCY SPECTRAL ENVELOPE ESTIMATION

An algorithm for posterior-warped log-frequency regular-

ized spectral envelope estimation is proposed. Given a set

of harmonic partial sequences (HPS) in the log-frequency

domain, the algorithm estimates the log-frequency envelope

using linear regularized discrete cepstrum estimation. In [40]

a method for estimating the spectral envelope using discrete

cepstrum coefficients in the Mel-scale was proposed. The

superiority of discrete cepstrum over the continuous cepstrum

coefficients and the linear prediction coefficients for spectral

envelope estimation was argued in [41]. Other methods for

envelope estimation in the linear frequency domain include

a weighted maximum likelihood spectral envelope estimation

technique in [42], which was employed for multiple-F0 es-

timation experiments in [6]. To the authors’ knowledge, no

other log-frequency harmonic envelope estimation algorithm

has been proposed in the literature. The proposed algorithm

can be outlined as follows:

1) Extract the harmonic partial sequence V [p,h] and corre-

sponding log-frequency bins kp,hfor a given pitch p and

harmonic index h = 1,...,13.

2) Convert the log-frequency bins kp,h to linear angular

frequencies ωp,h (where fs = 44.1 kHz and the lowest

frequency for analysis is flow= 27.5 Hz):

ωp,h= 27.5 ·2π

fs

· 2

kp,h

120

(32)

3) Perform spectral envelope estimation on V [p,h] and ωp,h

using linear regularized discrete cepstrum (estimate coeffi-

cients cp). Coefficients cpare estimated as:

cp= (MT

pMp+ ̺K)−1MT

pap

(33)

where

K = diag([0 1222··· (K − 1)2]), K is the cepstrum

order, ̺ is the regularization parameter, and

ap

= [ln(V [p,1])...ln(V [p,H])],

Mp=

1

...

1

2cos(ωp,1)

...

2cos(ωp,H)

···2cos(Kωp,1)

...

2cos(Kωp,H) ···

(34)

4) Estimate the vector of log-frequency discrete cepstral coef-

ficients dpfrom cp. In order to estimate dpfrom cp, we note

that the function which converts linear angular frequencies

into log-frequencies is given by:

g(ω) = 120 · log2

?

fs· ω

2π · 27.5

?

(35)

which is defined for ω ∈ [2π·27.5

normalized using ¯ g(ω) =

fs

,π]. Function g(ω) is

g(π)g(ω), which becomes:

π

¯ g(ω) =

π

2·27.5)· log2

log2(

fs

?

fs· ω

2π · 27.5

?

(36)

The

frequencies into angular linear frequencies is given by:

inverse function, which converts angular log-

¯ g−1(¯ ω) =2π · 27.5

fs

· 2

¯ ω log2(

fs

2·27.5)

π

(37)

Page 12

12IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING

k

RTFI Magnitude

0

200

400600

800

1000

0

0.5

1

1.5

2

2.5

Fig. 6.

The circle markers correspond to the detected overtones.

Log-frequency spectral envelope of an F#4 piano tone with P = 50.

which is defined in [0,π] → [2π·27.5

be seen that:

fs

,π]. From [40], it can

dp= A · cp

(38)

where

Ak+1,l+1=(2 − δ0l)

N

N−1

?

n=0

cos

?

l¯ g−1(πn

N)

?

cos

?πnk

N

?

(39)

where N is the size of the spectrum in samples, and k,l

range from 0 to P − 1.

5) Estimate the log-frequency spectral envelope SE from dp.

The log-frequency spectral envelope is defined as:

SEp(¯ ω) = exp

?

d0p+ 2

P−1

?

k=1

dkpcos(k¯ ω)

?

.

(40)

In Fig. 6, the warped log-frequency spectral envelope of an

F#4 note produced by a piano (from the MAPS dataset) is

depicted.

ACKNOWLEDGMENT

The authors would like to thank Valentin Emiya for gener-

ously providing the MAPS dataset. This work was supported

by a Westfield Trust Research Studentship (Queen Mary,

University of London).

REFERENCES

[1] J. P. Bello, “Towards the automated analysis of simple polyphonic

music: a knowledge-based approach,” Ph.D. dissertation, Department

of Electronc Engineering, Queen Mary, University of London, 2003.

[2] M. Goto, “A real-time music-scene-description system: predominant-

F0 estimation for detecting melody and bass lines in real-world audio

signals,” Speech Communication, vol. 43, pp. 311–329, 2004.

[3] A. Klapuri and M. Davy, Eds., Signal Processing Methods for Music

Transcription, 2nd ed. New York: Springer-Verlag, 2006.

[4] A. de Cheveign´ e, “Multiple F0 estimation,” in Computational Auditory

Scene Analysis, Algorithms and Applications, D. L. Wang and G. J.

Brown, Eds.IEEE Press/Wiley, 2006, pp. 45–79.

[5] P. Smaragdis, “Discovering auditory objects through non-negativity

constraints,” in ISCA Tutorial and Research Workshop on Statistical and

Perceptual Audition, Jeju, Korea, Oct. 2004.

[6] V. Emiya, R. Badeau, and B. David, “Multipitch estimation of piano

sounds using a new probabilistic spectral smoothness principle,” IEEE

Trans. Audio, Speech, and Language Processing, vol. 18, no. 6, pp.

1643–1654, Aug. 2010.

[7] G. Poliner and D. Ellis, “A discriminative model for polyphonic piano

transcription,” EURASIP J. Advances in Signal Processing, no. 8, pp.

154–162, Jan. 2007.

[8] F. Ca˜ nadas-Quesada, N. Ruiz-Reyes, P. V. Candeas, J. J. Carabias-

Orti, and S. Maldonado, “A multiple-F0 estimation approach based on

Gaussian spectral modelling for polyphonic music transcription,” J. New

Music Research, vol. 39, no. 1, pp. 93–107, Apr. 2010.

[9] C. Yeh, “Multiple fundamental frequency estimation of polyphonic

recordings,” Ph.D. dissertation, Universit´ e Paris VI - Pierre at Marie

Curie, France, Jun. 2008.

[10] R. Zhou, “Feature extraction of musical content for automatic music

transcription,” Ph.D. dissertation,´Ecole Polytechnique F´ ed´ erale de Lau-

sanne, Oct. 2006.

[11] E. Benetos and S. Dixon, “Multiple-F0 estimation of piano sounds ex-

ploiting spectral structure and temporal evolution,” in ISCA Tutorial and

Research Workshop on Statistical and Perceptual Audition, Makuhari,

Japan, Sep. 2010, pp. 13–18.

[12] ——, “Multiple fundamental frequency estimation using spectral struc-

ture and temporal evolution rules,” in Music Information Retrieval

Evaluation eXchange, Utrecht, Netherlands, Aug. 2010.

[13] J. Lafferty, A. McCallum, and F. Pereira, “Conditional random fields:

Probabilistic models for segmenting and labeling sequence data,” in 18th

Int. Conf. Machine Learning, San Francisco, USA, Jun. 2001, pp. 282–

289.

[14] M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, “RWC music

database: music genre database and musical instrument sound database,”

in Int. Conf. Music Information Retrieval, Baltimore, USA, Oct. 2003.

[15] “Music Information Retrieval Evaluation eXchange (MIREX).” [Online].

Available: http://music-ir.org/mirexwiki/

[16] A. Klapuri, “Sound onset detection by applying psychoacoustic knowl-

edge,” in IEEE Int. Conf. Acoustics, Speech, and Signal Processing,

Phoenix, USA, Mar. 1999, pp. 3089–3092.

[17] T. Tolonen and M. Karjalainen, “A computationally efficient multipitch

analysis model,” IEEE Trans. Speech and Audio Processing, vol. 8, no. 6,

pp. 708–716, Nov. 2000.

[18] A. Klapuri, “A method for visualizing the pitch content of polyphonic

music signals,” in 10th Int. Society for Music Information Retrieval

Conf., Kobe, Japan, Oct. 2009, pp. 615–620.

[19] J. C. Brown, “Computer identification of musical instruments using

pattern recognition with cepstral coefficients as features,” J. Acoustical

Society of America, vol. 105, no. 3, pp. 1933–1941, Mar. 1999.

[20] L. I. Ortiz-Berenguer, F. J. Casaj´ us-Quir´ os, M. Torres-Guijarro, and J. A.

Beracoechea, “Piano transcription using pattern recognition: aspects on

parameter extraction,” in Int. Conf. Digital Audio Effects, Naples, Italy,

Oct. 2004, pp. 212–216.

[21] E. Vincent, N. Bertin, and R. Badeau, “Adaptive harmonic spectral de-

composition for multiple pitch estimation,” IEEE Trans. Audio, Speech,

and Language Processing, vol. 18, no. 3, pp. 528–537, Mar. 2010.

[22] D. Tidhar, M. Mauch, and S. Dixon, “High precision frequency estima-

tion for harpsichord tuning classification,” in IEEE Int. Conf. Acoustics,

Speech and Signal Processing, Dallas, USA, Mar. 2010, pp. 61–64.

[23] A. Pertusa and J. M. I˜ nesta, “Multiple fundamental frequency estimation

using Gaussian smoothness,” in IEEE Int. Conf. Acoustics, Speech, and

Signal Processing, Las Vegas, USA, Apr. 2008, pp. 105–108.

[24] A. Klapuri, “Multiple fundamental frequency estimation based on har-

monicity and spectral smoothness,” IEEE Trans. Speech and Audio

Processing, vol. 11, no. 6, pp. 804–816, Nov. 2003.

[25] T. Virtanen and A. Klapuri, “Separation of harmonic sounds using linear

models for the overtone series,” in IEEE Int. Conf. Acoustics, Speech,

and Signal Processing, vol. 2, Orlando, USA, May 2002, pp. 1757–1760.

[26] M. R. Every and J. E. Szymanski, “Separation of synchronous pitched

notes by spectral filtering of harmonics,” IEEE Trans. Audio, Speech,

and Language Processing, vol. 14, no. 5, pp. 1845–1856, Sep. 2006.

[27] C. Uhle, “An investigation of low-level signal descriptors characterizing

the noiselike nature of an audio signal,” in Audio Engineering Society

128th Convention, London, UK, May 2010.

[28] Y. Li, J. Woodruff, and D. L. Wang, “Monaural musical sound separation

based on pitch and common amplitude modulation,” IEEE Trans. Audio,

Speech, and Language Processing, vol. 17, no. 7, pp. 1361–1371, Sep.

2009.

[29] L. de Lathauwer, “Signal processing based on multilinear algebra,” Ph.D.

dissertation, K. U. Leuven, Belgium, 1997.

[30] J. A. Nelder and R. Mead, “A simplex method for function minimiza-

tion,” Computer J., vol. 7, pp. 308–313, 1965.

[31] L. R. Rabiner, “A tutorial on hidden Markov models and selected

applications in speech recognition,” Proceedings of the IEEE, vol. 77,

no. 2, pp. 257–286, Feb. 1989.

[32] M. Ryyn¨ anen and A. Klapuri, “Polyphonic music transciption using note

event modeling,” in 2005 IEEE Workshop on Applications of Signal

Page 13

BENETOS AND DIXON: JOINT MULTI-PITCH DETECTION USING HARMONIC ENVELOPE ESTIMATION FOR POLYPHONIC MUSIC TRANSCRIPTION 13

Processing to Audio and Acoustics, New Paltz, USA, Oct. 2005, pp.

319–322.

[33] V. Emiya, R. Badeau, and B. David, “Automatic transcription of piano

music based on HMM tracking of jointly estimated pitches,” in European

Signal Processing Conf., Lausanne, Switzerland, Aug. 2008.

[34] H. Kameoka, T. Nishimoto, and S. Sagayama, “A multipitch analyzer

based on harmonic temporal structured clustering,” IEEE Trans. Audio,

Speech, and Language Processing, vol. 15, no. 3, pp. 982–994, Mar.

2007.

[35] S. Saito, H. Kameoka, K. Takahashi, T. Nishimoto, and S. Sagayama,

“Specmurt analysis of polyphonic music signals,” IEEE Trans. Audio,

Speech, and Language Processing, vol. 16, no. 3, pp. 639–650, Mar.

2008.

[36] “Sonic Visualiser 1.7.1.” [Online]. Available: http://www.sonicvisualiser.

org/

[37] S. Dixon, “On the computer recognition of solo piano music,” in 2000

Australasian Computer Music Conf., Jul. 2000, pp. 31–37.

[38] I. Guyon, J. Makhoul, R. Schwartz, and V. Vapnik, “What size test set

gives good error estimates?” IEEE Trans. Pattern Analysis and Machine

Intelligence, vol. 20, no. 1, pp. 52–64, Jan. 1998.

[39] M. Marolt, “A connectionist approach to automatic transcription of

polyphonic piano music,” IEEE Trans. Multimedia, vol. 6, no. 3, pp.

439–449, Jun. 2004.

[40] W. D’haes and X. Rodet, “Discrete cepstrum coefficients as perceptual

features,” in International Computer Music Conf., Sep. 2003.

[41] D. Schwarz and X. Rodet, “Spectral envelope estimation and represen-

tation for sound analysis-synthesis,” in International Computer Music

Conf., Beijing, China, Oct. 1999.

[42] R. Badeau and B. David, “Weighted maximum likelihood autoregressive

and moving average spectrum modeling,” in IEEE Int. Conf. Acoustics,

Speech, and Signal Processing, Las Vegas, USA, Apr. 2008, pp. 3761–

3764.

Emmanouil Benetos (S’09) received the B.Sc. de-

gree in informatics and the M.Sc. degree in digital

media from the Aristotle University of Thessaloniki,

Greece, in 2005 and 2007, respectively. In 2008,

he was with the Multimedia Informatics Lab, De-

partment of Computer Science, University of Crete,

Greece. He is currently pursuing the Ph.D. degree at

the Centre for Digital Music, Queen Mary University

of London, U.K., in the field of automatic music

transcription. His research interests include music

and speech signal processing and machine learning.

Mr. Benetos is a member of the Alexander S. Onassis Scholars Association.

Simon Dixon leads the Music Informatics area at

the Centre for Digital Music, Queen Mary Uni-

versity of London. His research interests are fo-

cussed on accessing and manipulating musical con-

tent and knowledge, and involve music signal anal-

ysis, knowledge representation and semantic web

technologies. He has a particular interest in high-

level aspects of music such as rhythm and harmony,

and has published research on beat tracking, audio

alignment, chord and note transcription, characteri-

sation of musical style, analysis of expressive per-

formance, and the use of technology in musicology and music education. He

is author of the beat tracking software BeatRoot and the audio alignment

software MATCH. He was Programme Chair for ISMIR 2007, and General

Co-chair of the 2011 Dagstuhl Seminar on Multimodal Music Processing, and

has published over 80 papers in the area of music informatics.

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