Content uploaded by Estefanía Cano
Author content
All content in this area was uploaded by Estefanía Cano on May 21, 2015
Content may be subject to copyright.
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-6, 2013
RE-THINKING SOUND SEPARATION: PRIOR INFORMATION AND ADDITIVITY
CONSTRAINT IN SEPARATION ALGORITHMS
Estefanía Cano & Christian Dittmar
Semantic Music Technologies
Fraunhofer IDMT
Ilmenau, Germany
[cano,dmr]@idmt.fraunhofer.de
Gerald Schuller
Institute for Media Technology
Ilmenau University of Technology
Ilmenau, Germany
gerald.schuller@tu-ilmenau.de
ABSTRACT
In this paper, we study the effect of prior information on the quality
of informed source separation algorithms. We present results with
our system for solo and accompaniment separation and contrast
our findings with two other state-of-the art approaches. Results
suggest current separation techniques limit performance when com-
pared to extraction process of prior information. Furthermore, we
present an alternative view of the separation process where the ad-
ditivity constraint of the algorithm is removed in the attempt to
maximize obtained quality. Plausible future directions in sound
separation research are discussed.
1. INTRODUCTION
Sound source separation deals with the extraction of independent
sound sources from an audio mix. To address this problem, many
approaches have been proposed in the literature: filtering and mask-
ing techniques, statistical approaches, perceptually motivated sys-
tems, time-frequency representation and signal models are some
of the techniques used. However even today, sound separation is
still considered an unsolved problem. Separation quality of state-
of-the-art systems is very limited and dependent on the type of sig-
nals used. Currently, there is still no clear direction for a general
solution to this problem.
After many years of research, results in the field suggest that
separation performance can be improved when prior information
about the sources is available. The inclusion of known informa-
tion about the sources in the separation scheme is referred to as
Informed Sound Source Separation (ISS) and comprises, among
others, the use of MIDI-like musical scores, the use of pitch tracks
of one or several sources, oracle sound separation where the orig-
inal sources are available, and the extraction of model parameters
from training data of a particular sound source.
2. PAPER OUTLINE AND MOTIVATION
In Sec. 3 we present a general overview of the state-of-the-art in
sound source separation. In an attempt to further understand the
potential of current algorithms for informed sound separation, we
ask ourselves the questions: How far can we get by using prior in-
formation as pitch or musical scores? What could be the expected
quality improvement of separation algorithms if we could provide
more accurate prior-information of this kind? To address these
questions, we discuss in Sec. 5.1 results from three state-of-the-art
systems for informed source separation when ground truth (or very
accurate) prior information is available. In Sec. 5.2 we use the in-
sights obtained in the previous analyses to propose an alternative
to sound source separation. We also address the fundamental goal
of sound separation in an attempt to get some insight for future
research directions. Concluding remarks are presented in Sec. 6
3. STATE-OF-THE-ART
In general, source separation approaches can be classified accord-
ing to the processing technique used. Three main categories exist:
statistical approaches, classical signal processing approaches, and
computational auditory scene analysis (CASA) approaches. Sta-
tistical techniques for sound separation generally assume certain
statistical properties of the sound sources. Systems based on In-
dependent Subspace Analysis (ISA) [1], [2], Non-Negative Matrix
Factorization (NMF) [3], [4], [5], tensor factorization[6], [7], and
sparse coding [8], [9], [10] have been proposed. In the case of sig-
nal processing approaches for sound separation, different forms of
masking and filtering techniques to extract the desired sources are
used [11], [12]. Computational auditory scene analysis (CASA)
techniques have also been proposed [13], [14].
Many systems for sound source separation have attempted to
use pitch as prior information.These systems are based on the as-
sumption that every sound source follows a defined pitch sequence
over time. The system described in [15] proposes an invertible
mid-level representation of the audio signal which gives access
to some semantically rich salience functions for pitch and timbre
content analysis. The system uses an instantaneous signal model
(IMM) which represents the audio signal as the sum of a signal
of interest, i.e., the lead instrument, and a residual, i.e., accom-
paniment. A source-filter model is used to represent the signal of
interest. Information from the source is related to the pitch of the
lead instrument and information from the filter is related to the tim-
bre of the instrument. The residual is modeled using non-negative
matrix factorization (NMF). The mid-level representation is used
to separate lead instrument form accompaniment in conjunction
with a Wiener masking approach. In [16], an approach for singing
voice extraction in stereo recordings that uses panning information
and a probabilistic pitch tracking approach is presented. Some ap-
proaches have been proposed for supervised pitch extraction with
a subsequent separation scheme [17], [18], [19], [20].
Score-informed source separation extracts audio sources from
the mix by using a MIDI-like score representation of the desired
source(s). A score-informed source separation algorithm is pre-
sented in [21]. This system attempts to separate solo instruments
from their accompaniment using MIDI-like scores of the lead in-
strument as prior information. The approach uses chroma-based
DAFX-1
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-5, 2013
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-6, 2013
dynamic time warping (DTW) to address global misalignments be-
tween the score and the audio signal. Furthermore, a MIDI confi-
dence measure is proposed to deal with small-scale misalignments.
In [22] a score-informed separation algorithm is described, which
is based on Probabilistic Latent Component Analysis (PLCA) and
the use of synthesized versions of the score as prior distributions
in the PLCA decomposition of the original mix.
4. PROPOSED ALGORITHM
In this section, we describe two algorithms used for the experi-
ments described in sections 5.1 and 5.2. The results from the dif-
ferent experiments are used to address the questions posed in the
motivation in Sec. 2.
In [23], we propose a pitch-informed method to separate solo
instruments from accompaniment. Pitch information from the solo
instrument is extracted with an approach described in [24]. The
rough pitch estimates are refined using a linear interpolation ap-
proach where the energy of the fundamental frequency and its har-
monic components is calculated for each interpolation step. The
maximum energy is taken as an indicator of the new fundamen-
tal frequency. A harmonic component refinement stage iteratively
constructs a harmonic series for each fundamental frequency us-
ing known acoustical characteristics of musical instruments. Ini-
tial binary masks are created based on the iterative estimation and
a post-processing stage is used to take care of attack frames and re-
duce interference from percussive sources. After post-processing,
masks are no longer binary. Solo and accompaniment sources are
re-synthesized using the obtained masks. For the remainder of this
paper, this algorithm will be referred to as Cano1.
Furthermore, we present a basic modification that makes our
algorithm more suited for vocal extraction. This algorithm will be
referred to as Cano2 and simply modifies the estimation stage by
including a noise spectrum in the Harmonic Refinement stage to
capture characteristic noise-like sounds in vocal signals. Similar
approaches have also been used in [17].
5. EXPERIMENTS & DISCUSSION
For the experiments conducted in this paper, a dataset of 10 multi-
track recordings was used. These recording are part of the PEASS
1and BSS 2datasets and are freely available for download un-
der CC license. All the signals in the dataset are vocal tracks
(male or female) with accompaniment. For all signals, the multi-
track recordings were mixed to obtain accompaniment tracks, solo
tracks, and a final monaural mix. The signals are described in Ta-
ble 1.
Recognizing the importance of including perceptual aspects
in the evaluation of sound separation results, we use the PEASS
Toolkit- Perceptual Evaluation Methods for Audio Source Separa-
tion [25] to measure quality of the separated signals.The PEASS
Toolkit presents a set of four objective perceptual measures for
separation quality assessment, i.e., Overall Perceptual Score (OPS),
Target Perceptual Score (TPS), Interference Perceptual Score (IPS),
Artifact Perceptual Score (APS). For reference purposes, we also
1http://sisec.wiki.irisa.fr/tiki-index.php?
page=Professionally+produced+music+recordings
2http://bass-db.gforge.inria.fr/BASS- dB/?show=
browse&id=mtracks
present common objective scores based on energy ratios, i.e., Sig-
nal to Distortion Ratio (SDR), Image to Spatial Distortion Ratio
(ISR), Signal to Interference Ratio (SIR), Signal to Artifact Ratio
(SAR).
5.1. Prior Information in Separation Algorithms
In this section, we evaluate the effects of prior information on the
quality of the proposed approach and contrast our finding with two
other state-of-the-art algorithms.
We refer to our previous work on pitch-informed sound sepa-
ration (Cano1) to separate audio recordings into solo and accom-
paniment tracks. It is to be noted that both the pitch extraction and
separation stages in Cano1 are completely automatic. To create
ground truth information, the Songs2See Editor [19] was used to
manually correct and refine the pitch extraction of the solo instru-
ment. We used the corrected pitch sequences to feed our separation
algorithm and obtain solo and accompaniment tracks by bypassing
the automatic pitch detection stage. The goal of this experiment is
to assess the potential of the separation algorithm when accurate
prior information is available. The algorithm that uses ground truth
pitch information will be referred to as CanoU.
For this study, signals 1, 8, and 9 from our dataset were se-
lected to obtain ground truth pitch information and perform sepa-
ration with the two algorithms ( Cano1,CanoU). Results are pre-
sented in Table 2. Perceptual measures tend to evidence a qual-
ity improvement when accurate pitch information is provided to
the algorithm. However, the more interesting observation is the
fact that even though there is a quality improvement, results are
far from reaching maximum quality scores. The maximum Over-
all Perceptual Score (OPS) obtained was 32.02 for accomp9 and
high Interference Perceptual Scores (IPS) are obtained in general,
reaching the highest score of 74.8 in solo1. The highest scores ob-
tained for the four perceptual measures are written in bold font in
Table 2.
Given that these results only represent the particular case of
our algorithm and cannot be generalized, we revisit the results pre-
sented by [15] and [21] to get a wider view of the performance of
separation algorithms. The details of these algorithms were briefly
described in Sec. 3. For the remainder of this paper, these al-
gorithms will be referred to as Durrieu and Bosch respectively.
In both cases, a system for solo/accompaniment separation is pre-
sented. Similarly, the authors present results comparing the perfor-
mance of the fully automatic algorithm with the one obtained when
ground truth information is available. In [17], Durrieu presents an
user-assisted algorithm where the pitch track of the solo instru-
ment can be refined using a specially designed user interface. This
algorithm will be referred to as DurrieuU. In the case of [21], the
authors manually align the scores to the audio tracks and use them
as ground truth information. The user-assisted version of this al-
gorithm will be referred to as BoschU.
In Table 3 we show some of the results presented by the au-
thors related to these four algorithms. In the case of Durrieu and
DurrieuU, we present the average scores for solo extraction ob-
tained in the 2011 Signal Separation Evaluation Campaign (SiSEC11),
which can be accessed in the campaign’s website 3. In this case,
both the PEASS measures and the energy ratios are available. In
the case of Bosch and BoschU, the results for solo and accompa-
niment extraction for the dataset S1-D3 using a Wall-N mask are
3http://www.irisa.fr/metiss/SiSEC11/
professional/test_eval2011.htm
DAFX-2
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-5, 2013
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-6, 2013
Table 1: Data set used: In the text, signal numbers are used to refer to each of the signals.
Signal Num. Name Segment [sec]
1 Bearlin Roads 85 - 99
2 Tamy que pena tanto faz 6 - 19
3 Another Dreamer 69 - 94
4 Ultimate nz tour 43 - 61
5 Dreams 0 - 35
6 Life as a disturbed infobeing 0 - 57
7 Mix Tape 7 - 53
8 The ones we love 32 - 48
9 We weren’t there 0 - 32
10 Wreck 15 - 34
Table 2: Signal version obtained with the different masking and post-processing approaches
Sig. Num Source Alg. OPS TPS IPS APS SDR ISR SIR SAR
1
solo Cano1 20.01 2.41 72.25 6.52 -5.19 -4.15 5.26 9.85
CanoU 22.09 3.17 74.8 9.21 -6.71 -5.99 8.38 11.58
accomp Cano1 24.99 34.38 57.30 36.97 -3.37 -3.25 12.66 15.55
CanoU 27.08 39.63 54.31 40.64 -3.544 -3.42 13.02 17.13
8
solo Cano1 19.31 2.60 51.16 9.36 -4.91 -3.70 3.17 11.368
CanoU 23.23 3.20 72.73 8.49 -4.43 -3.72 6.47 12.67
accomp Cano1 26.60 43.46 62.41 42.53 -3.54 -3.28 10.73 13.46
CanoU 30.73 56.13 55.33 48.41 -3.623 -3.41 12.65 14.66
9
solo Cano1 25.66 0.96 67.7 2.23 -3.64 -3.11 8.47 10.23
CanoU 18.05 3.8 61.31 10.83 -3.72 -3.01 5.81 11.95
accomp Cano1 30.84 29.03 59.74 33.41 -3.44 -3.15 9.87 14.48
CanoU 32.02 30.44 53.96 33.69 -2.81 -2.55 9.59 15.12
presented. In this case only the Signal to Distortion Ratio (SDR)
is available. We refer the reader to [21] for details. It is important
to bear in mind that the three algorithms presented in this Section
(Cano, Durrieu, Bosch) make use of different datasets and their re-
sults cannot be used for direct comparison. The goal of presenting
these results is to describe a similar phenomenon occurring in dif-
ferent algorithms but not to perform a direct comparison between
them.
A similar behavior is observed in both of the comparison al-
gorithms. The use of ground truth prior information tends to result
in higher quality measures. However, two important observations
can be made: (1) Scores with ground truth information are still
far from reaching maximum levels, and (2) Quality differences be-
tween ground truth and automatically extracted pitch are marginal.
In general, results reveal that there is still much room for im-
provement when it comes to informed sound separation algorithms.
Including prior information obviously benefits performance but it
is hard to envision a generalized and robust solution given cur-
rent results. This leads us to two possible paths: (1) On the one
hand we could consider the possibility that the type of prior infor-
mation that we are currently using does not carry enough signal
details to allow robust separation and consider possibilities to en-
hance the information used. Taking into account the great diversity
not only of musical signals, but also of playing styles, genres, and
recording conditions which a separation algorithm can encounter,
the expectation that such general information as pitch could suf-
fice to guide the separation schemes, comes short. We could then
consider including, besides pitch information, other types of infor-
mation that allow better characterization of sound sources. This
would naturally lead to the development of target-designed algo-
rithms optimized for the extraction of a particular class of signals.
The use of instrument-specific information and instrument models
within the separation schemes could be an option as for example
in [26]. (2) On the other hand we could consider the possibility to
further improve our current sound analysis and synthesis methods
so that more accurate information can be extracted. Works on this
topic include developments of reassignment and derivative meth-
ods among others [27]. In [28] and [29], the authors already rec-
ognize the limitations of current analysis techniques and propose
an informed-analysis front-end to improve sound source separa-
tion. In these approaches prior information is included directly in
the analysis in the form of watermarks and bits of ground truth in-
formation, respectively. In both cases, separation results evidence
an improvement. However, theses approaches have a limitation in
the sense that the original signals need to be available to extract
the prior information inserted in the analysis. Having the original,
DAFX-3
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-5, 2013
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-6, 2013
Table 3: Comparison between two automatic algorithms and their corresponding user-assisted versions
Alg. OPS TPS IPS APS SDR ISR SIR SAR
Durrieu [solo] 22.4 28.8 59.0 30.8 3.8 6.2 Inf 3.1
DurrieuU [solo] 26.0 28.4 61.1 29.9 5.4 8.3 Inf 5.4
Bosch [Accom/solo] - - - - 10.35/6.20 - - -
BoschU [Accom/solo] - - - - 10.46/6.31 - - -
unmixed signals is not always possible.
There is however an alternative possibility to be explored. Re-
sults have shown that not only is the available information about
the sources of critical importance for separation performance, but
also the mechanisms used to include such information in the sep-
aration scheme. Including prior information in the time-frequency
domain (like pitch or MIDI scores) has proven to contribute to the
quality of sound separation. Including information in the analy-
sis stage (like watermarks and bits of information) has also proven
to improve separation. This option with the difficulty of requiring
the original sources to extract the prior information. The third op-
tion is then naturally, including prior information about the sources
in the synthesis stage. Here again, separation algorithms would be
optimized to deal with a particular class of signals. Instrument syn-
thesis models, developed and trained off-line, could be used to re-
create the original signals as closely as possible. This option leaves
open the possibility to include information in the time-frequency
transform coming from domains as pitch, timbre, scores, etc. Fur-
thermore, having the original signals would not be required. An-
other important characteristic of such an approach would be that
a strict constraint to exactly reconstruct the original mix from the
extracted sources, could not be set. In the remainder of this pa-
per, the hard constraint imposed to most separation algorithms to
exactly reconstruct the mix from the extracted sources is referred
to as additivity constraint. With the third option, the analysis and
synthesis stages of separation algorithms would most likely use
different signal processing techniques and such constraint would
be difficult to impose.
5.2. Redefining Sound Separation
After the discussion presented in Sec. 5.1, there is still an open
question that we wish to address: Is there any possibility to obtain
a performance gain with our current separation approaches without
fundamentally changing them?
To address this question, we created different versions of our
separation algorithm which are described in Table 4. For each ver-
sion, the pitch detection and spectral component estimation is kept
unchanged, but different spectral masking techniques are used to
obtain the resulting signals. For the cases where Wiener filtering is
used, pdenotes the power to which each individual element of the
spectrograms are raised. In versions 1 and 6, the Post-Processing
stage of the algorithm is bypassed to allow binary and Wiener
masking respectively. We advise the reader to refer to [23] for
algorithm details. As can be seen, the modifications performed to
the algorithm are rather basic and do not fundamentally change the
original system. However, each one of them has clear effects on
its performance.
For this experiment we use the ten tracks from the dataset
described in Table 1 and for each one of them, solo and accom-
paniment tracks are extracted using each of the seven algorithm
versions. As in Sec. 5.1, the PEASS Toolkit is used for qual-
ity evaluation and measures based on energy ratios are presented
for reference. Separation results for the solo and accompaniment
signals using the PEASS Toolkit are presented in Figures 1 and
2 respectively. Similarly, results for the solo and accompaniment
signals using the energy ratios are presented in Figures 3 and 4.
In all figures, the mean performance over the ten signals is pre-
sented for the seven algorithm versions. The whiskers indicate the
standard deviation of each version and the highest score among all
versions, is shown with an inner (blue) dot in the marker.
PEASS Quality Measures: Solo
012345678
−5
0
5
10
15
20 APS
012345678
45
50
55
60
65
70
75 IPS
012345678
−5
0
5
10
15 TPS
012345678
19
20
21
22
23
24
25
26
27
28 OPS
Figure 1: Mean perceptual scores for the solo signals : Overall
Perceptual Score (OPS), Target Perceptual Score (TPS), Interfer-
ence Perceptual Score (IPS), Artifact Perceptual Score (APS).
As can be seen in Fig. 1, for three of the four perceptual
scores (OPS, TPS, APS) for the solo tracks, the highest mean per-
formance is obtained with the Cano2 algorithm (version 5). This
is however an expected result, as the algorithm was specifically
modified to better handle vocal signals. On the other hand, results
for the accompaniment tracks differ. As shown in Fig. 2, the high-
est scores are obtained for three of the measures (OPS, TPS, APS)
with different versions of the Cano1 algorithm. For the Interfer-
ence Perceptual Score (IPS), the highest mean performance is ob-
tained with the Cano2 algorithm. This result further evidences that
DAFX-4
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-5, 2013
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-6, 2013
Table 4: Signal version obtained with the different masking and post-processing approaches
Version Description
1 Binary
2 Cano1
3 Cano1 + Wiener [p=2]
4 Cano2 + Wiener [p=2]
5 Cano2
6 Wiener [p=0.3]
7 Cano1 + Wiener [p=0.3]
PEASS Quality Measures: Accompaniment
012345678
30
35
40
45
50
55 APS
012345678
35
40
45
50
55
60
65
70
75 IPS
012345678
25
30
35
40
45
50
55 TPS
012345678
20
22
24
26
28
30
32 OPS
Figure 2: Mean perceptual scores for the accompaniment sig-
nals: Overall Perceptual Score (OPS), Target Perceptual Score
(TPS), Interference Perceptual Score (IPS), Artifact Perceptual
Score (APS).
better solo extraction is obtained with the Cano2 algorithm as for
the backing track, the vocal signal is, in this case, the only source
of interference.
Results suggest that for the particular task of solo and accom-
paniment separation, the highest perceptual scores can be obtained
differently for each of the desired sources. Algorithm modifica-
tions that might benefit solo extraction can potentially have a neg-
ative effect in the performance for accompaniment extraction. In
this line of thought, we conducted informal tests using the Cano2
algorithm to extract solo tracks from different musical instruments
(clarinet, trumpet, and saxophone). In all cases, performance of
solo extraction suggested a performance decrease. This further
confirms the idea that for our current approach, performance might
be maximized if different versions of the algorithm are used for the
solo and accompaniment tracks. This brings us back to the con-
cept of additivity constraint presented in Sec.5.1. Bearing in mind
the possibilities and limitations of our current sound analysis tech-
niques, and the fact that theoretical bound exist for them, allowing
BSS Quality Measures: Solo
012345678
7
8
9
10
11
12
13
14
15 SAR
012345678
0
2
4
6
8
10
12 SIR
012345678
−9
−8
−7
−6
−5
−4
−3
−2
−1 ISR
012345678
−10
−9
−8
−7
−6
−5
−4
−3
−2 SDR
Figure 3: Mean energy ratios for the solo signals: Signal to Dis-
tortion Ratio (SDR), Image to Spatial Distortion Ratio (ISR), Sig-
nal to Interference Ratio (SIR), Signal to Artifact Ratio (SAR).
separation algorithms to extract sources with the goal of maximiz-
ing perceptual quality instead of reconstructing the original mix,
might bring us better final results.
Following this line of thought, the idea of moving from sep-
aration to understanding presented in a keynote presentation by
Smaragdis 4, becomes relevant. In most cases, source separation
is not the final goal but most likely, an intermediate step to further
types of processing: more accurate music transcription, re-mixing
audio tracks, audio classification, etc. In that sense, extracting the
exact original source might not even be necessary for the final ap-
plication. Different quality requirements for different applications
might be needed: music transcription, for example, would most
likely require high Interference Perceptual Scores (IPS) for robust
performance, as pitch tracks of the independent sources are the fi-
nal goal. On the other hand, IPS requirements might not be so
strict when it comes to re-mixing audio tracks. Minimizing arti-
4http://www.cs.illinois.edu/~paris/pubs/
smaragdis-LVAICA12.pdf
DAFX-5
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-5, 2013
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-6, 2013
BSS Quality Measures: Accompaniment
012345678
6
8
10
12
14
16
18 SAR
012345678
2
4
6
8
10
12 SIR
012345678
−9
−8
−7
−6
−5
−4
−3
−2
−1
0ISR
012345678
−10
−8
−6
−4
−2
0SDR
Figure 4: Mean energy ratios for the accompaniment signals: Sig-
nal to Distortion Ratio (SDR), Image to Spatial Distortion Ratio
(ISR), Signal to Interference Ratio (SIR), Signal to Artifact Ratio
(SAR).
facts and preserving the sources are probably more relevant and
consequently higher APS and TPS might be required. Thus, con-
sidering the final processing goal and its quality requirements, in-
stead of focusing on the separation task only, might bring better
overall results and open possibilities for further analyses.
6. CONCLUDING REMARKS
We have addressed two defining topics in informed sound separa-
tion research: (1) The effects of pitch and score information in the
performance of separation algorithms were studied showing that
attainable quality, when accurate prior information is available,
still fails to reach maximal scores. We review three possibilities
to include prior information in separation approaches; namely in
the analysis , time-frequency transform, and synthesis stages. Due
to the processing possibilities and flexibility that it provides to the
separation scheme, we see great potential in including information
directly in the synthesis stage. Future work will be conducted in
this direction. (2) We propose the possibility to remove the addi-
tivity constraint to improve quality of separation and as a future di-
rection where algorithms could have completely independent anal-
ysis and synthesis approaches. In this sense we redefine our goal
with separation research from extracting the original sources from
the mix, to obtaining sources that meet perceptual quality require-
ments imposed for different applications and that allow the use of
separation schemes as intermediate processing steps.
7. REFERENCES
[1] Derry FitzGerald, R Lawlor, and Eugene Coyle, “Sub-band
independent subspace analysis for drum transcription,” in 5th
International Conference on Digital Audio Effects (DAFx-
02), Hamburg, Germany, 2002, number 5, pp. 1–5.
[2] Christian Uhle, C Dittmar, and T Sporer, “Extraction of
drum tracks from polyphonic music using independent sub-
space analysis,” in 4th International Symposium on Indepen-
dent Component Analysis and Blind Signal Separation (ICA
2003), Nara, Japan, 2003, pp. 843–848.
[3] Romain Hennequin, Bertrand David, and Roland Badeau,
“Score informed audio source separation using a paramet-
ric model of non-negative spectrogram,” in IEEE Interna-
tional Conference on Acoustics, Speech, and Signal Process-
ing (ICASSP 2011), Prague, Czech Republic, 2011, num-
ber 1, pp. 45–48.
[4] S. Kirbiz and Paris Smaragdis, “Adaptive time-frequency
resolution for single channel sound source separation based
on non-negative matrix factorization,” in IEEE 19th Sig-
nal Processing and Communications Applications Confer-
ence (SIU 2011), Antalya, Turkey, 2011, pp. 964–967.
[5] A. Lefevre, Francis Bach, and C Févotte, “Itakura-Saito non-
negative matrix factorization with group sparsity,” in IEEE
International Conference on Acoustics, Speech, and Signal
Processing (ICASSP 2011), Prague, Czech Republic, 2011,
number 1, pp. 21–24.
[6] Derry Fitzgerald, Matt Cranitch, and Eugene Coyle, “Us-
ing tensor factorisation models to separate drums from poly-
phonic music,” in 12th International Conference on Digital
Audio Effects (DAFx-09), Como, Italy, 2009, pp. 1–5.
[7] U. Simsekli and A. Cemgil, “Score guided musical source
separation using generalized coupled tensor factorization,”
in 20th European Signal Processing Conference (EUSIPCO
2012), Bucharest, Romania, 2012, number Eusipco, pp.
2639–2643.
[8] Mark D. Plumbley, Thomas Blumensath, Laurent Daudet,
Rémi Gribonval, and Mike Davies, “Sparse representations
in audio and music: from coding to source separation,” Pro-
ceedings of the IEEE, vol. 98, no. 6, pp. 995–1005, 2010.
[9] Samer a Abdallah and Mark D Plumbley, “Unsupervised
analysis of polyphonic music by sparse coding.,” IEEE trans-
actions on neural networks / a publication of the IEEE Neu-
ral Networks Council, vol. 17, no. 1, pp. 179–96, Jan. 2006.
[10] Rémi Gribonval and Synlvain Lesage, “A survey of sparse
component analysis for blind source separation: principles,
perspectives, and new challenges,” in European Symposium
on Artificial Neural Networks (ESANN 2006), Bruges, Bel-
gium, 2006, number April.
[11] Derry Fitzgerald, “Harmonic/percussive separation using
median filtering,” in 13th International Conference on Digi-
tal Audio Effects (DAFx-10), Graz, Austria, 2010, number 1,
pp. 10–13.
[12] David Gunawan and Deep Sen, “Separation of Harmonic
Musical Instrument Notes Using Spectro-Temporal Mod-
eling of Harmonic Magnitudes and Spectrogram Inversion
with Phase Optimization,” Journal of the Audio Engineering
Society (AES), vol. 60, no. 12, 2012.
[13] L. Drake, J. Rutledge, J. Zhang, and a Katsaggelos, “A Com-
putational Auditory Scene Analysis-Enhanced Beamforming
Approach for Sound Source Separation,” EURASIP Jour-
nal on Advances in Signal Processing, vol. 2009, no. 1, pp.
403681, 2009.
DAFX-6
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-5, 2013
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-6, 2013
[14] Yipeng Li, John Woodruff, and D. Wang, “Monaural musi-
cal sound separation based on pitch and common amplitude
modulation,” IEEE Transactions on Acoustics, Speech, and
Signal Processing, vol. 17, no. 7, pp. 1361–1371, 2009.
[15] Jean-Louis Durrieu, Bertrand David, and Gaël Richard, “A
musically motivated mid-level representation for pitch esti-
mation and musical audio source separation,” IEEE Journal
of Selected Topics in Signal Processing, vol. 5, no. 6, pp.
1180–1191, 2011.
[16] Ricard Marxer, Jordi Janer, and Jordi Bonada, “Low-Latency
instrument separation in polyphonic audio using timbre mod-
els,” Latent Variable Analysis and Signal Separation, vol.
7191, pp. 314–321, 2012.
[17] Jean-Louis Durrieu and Jean-Philippe Thiran, “Musical au-
dio source separation based on user-selected f0 track,” Latent
Variable Analysis and Signal Separation, , no. 1, pp. 1–8,
2012.
[18] Derry FitzGerald, “User assisted separation using tensor
factorisation,” in 20th European Signal Processing Con-
ference (EUSIPCO 2012), Bucharest, Romania, 2012, pp.
2412–2416.
[19] Estefanía Cano, Christian Dittmar, and Sascha Grollmisch,
“Songs2See: Learn to Play by Playing,” in 12th International
Society for Music Information Retrieval Conference (ISMIR
2011), Miami, USA, 2011.
[20] Benoit Fuentes, Roland Badeau, and Gaël Richard, “Blind
Harmonic Adaptive Decomposition Applied to Supervised
Source Separation,” in 2009 Ninth IEEE International Con-
ference on Advanced Learning Technologies, Bucharest, Ro-
mania, 2012, number Eusipco, pp. 2654–2658.
[21] Juan J. Bosch, K. Kondo, R. Marxer, and J. Janer, “Score-
informed and timbre independent lead instrument separation
in real-world scenarios,” in 20th European Signal Processing
Conference (EUSIPCO 2012), Bucharest, Romania, 2012,
vol. 25, pp. 2417–2421.
[22] Joachim Ganseman, Paul Scheunders, Gautham J. Mysore,
and Jonathan S. Abel, “Evaluation of a score-informed
source separation system,” in 11th International Society
for Music Information Retrieval Conference (ISMIR 2010),
Utrecht, Netherlands, 2010.
[23] Estefanía Cano, Christian Dittmar, and Gerald Schuller, “Ef-
ficient Implementation of a System for Solo and Accompa-
niment Separation in Polyphonic Music,” in 20th European
Signal Processing Conference (EUSIPCO 2012), Bucharest,
Romania, 2012, pp. 285–289.
[24] Karin Dressler, “Pitch Estimation by pair-wise Evaluation of
Spectral Peaks,” in Proceedings of the AES 42nd Conference
on Semantic Audio, Ilmenau, 2011, pp. 278–290.
[25] Valentin Emiya, Emmanuel Vincent, Niklas Harlander, and
Volker Hohmann, “Subjective and Objective Quality Assess-
ment of Audio Source Separation,” IEEE Transactions on
Audio, Speech, and Language Processing, vol. 19, no. 7, pp.
2046–2057, Sept. 2011.
[26] Mathieu Coïc and Juan José Burred, “Bayesian Non-negative
Matrix Factorization with Learned Temporal Smoothness
Priors,” in International Conference on Latent Variable
Analysis and Signal Separation (LVA/ICA), Tel-Aviv, Israel,
2012, pp. 280–287.
[27] Sylvain Marchand and Philippe Depalle, “Generalization of
the derivative analysis method to non-stationary sinusoidal
modeling.,” in 11th International Conference on Digital Au-
dio Effects (DAFx-08), Espoo, Finland, 2008, pp. 1–8.
[28] Mathieu Parvaix, Laurent Girin, and Jean-Marc Brossier, “A
watermarking-based method for single-channel audio source
separation,” in IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2009), Taiper, Tai-
wan, 2009, pp. 101–104.
[29] Sylvain Marchand and Dominique Fourer, “Breaking the
bounds: Introducing informed spectral analysis,” in 13th In-
ternational Conference on Digital Audio Effects (DAFx-10),
Graz, Austria, 2010, pp. 1–8.
[30] MA Casey and A Westner, “Separation of mixed audio
sources by independent subspace analysis,” in International
Computer Music Conference (ICMC 2000), Berlin, Ger-
many, 2000.
[31] Niall Cahill, Rory Cooney, and Robert Lawlor, “An En-
hanced Implementation of the ADRess (Azimuth Discrim-
ination and Resynthesis) Music Source Separation Algo-
rithm,” in 121th Convention of the Audio Engineering So-
ciety (AES), San Francisco, USA, 2006.
DAFX-7
Proc. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-5, 2013
