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Identical sound detection of using Least Mean Square - Adaptive cross correlation for transcription
Abstract
Development of eastern music like gamelan is far lagged from that of western music because gamelan is still stigmatized as part of traditional arts which must be preserved instead of being analyzed and developed. Therefore development of in depth research concerning gamelan music is needed to bring back the greatness of this music like that in its era (17th-18th century). This research initiates the gamelan sound extraction for music transcription. We applied Least Mean Square - Adaptive Cross Correlation (LACC). ACC was conducted to generate spectral density for music transcription while LMS was utilized to detect instruments which have identical fundamental frequency, in order to avoid over detection. Experiment demonstrates the test performance demonstrates that the proposed method provided 2 - 12% improvement for real gamelan performance comparing to conventional methods such as STFT.
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The major time and frequency analysis methods that have been
applied to music processing are traced and application areas described.
Techniques are examined in the context of Cohen's class, facilitating
comparison and the design of new approaches. A trumpet example
illustrates most techniques. The impact of different analysis methods on
pitch and timbre examination is shown. Analyses spanning Fourier series
and transform, pitch synchronous analysis, heterodyne filter, short-time
Fourier transform (STFT), phase vocoder, constant-Q and wavelet
transforms, the Wigner (1932) distribution, and the modal distribution
are all covered. The limitations of windowing methods and their reliance
on steady-state assumptions and infinite duration sinusoids to define
frequency and amplitude are detailed. The Wigner distribution, in
contrast, uses the analytic signal to define instantaneous frequency and
power parameters. The modal distribution is shown to be a linear
transformation of the Wigner distribution optimized for estimating those
parameters for a musical signal model. Application areas consider
analysis, resynthesis, transcription, and visualization. The more
stringent requirements for time-frequency (TF) distributions in these
applications are compared with the weaker requirements found in speech
analysis and highlight the need for further theoretical research
A system for automatic identification of polyphonic piano recordings based on a heuristic approach is presented. The input to the transcription system is any piano piece sampled at 44.1 kHz. The system does not need any kind of training and there are no restrictions on the number of notes played simultaneously or on the notes' frequency range. The transcription system initially divides the piano piece into attack slots. This temporal segmentation is based on onset detection and it is made by means of a sliding window procedure. Afterwards, a frequency analysis is performed on each attack slot. Finally, musical parameters such as beat, time signature, key signature and tonality are determined. It is shown that the proposed system achieves very good performance using simple signal processing techniques.