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Abstract
In this paper, a Wavelet transform-based approach for estimation of multipitch in music signal has been proposed. Among the Morlet Wavelet (MW), Mexican hat, and Shannon wavelet that belong to the widely used wavelets in different applications, the Morlet wavelet performs well for estimation of pitch in polyphonic music signals. This is why a method involving modification of the Morlet wavelet has been proposed for achieving better accuracy in estimation of multiple pitches in polyphonic music. Performance of the Modified Morlet Wavelet (MMW) based Multipitch Estimation (MPE) scheme has been compared with that of a method based on Fast Fourier Transform and another based on the original Morlet Wavelet, in terms of percentage Gross Pitch Error (GPE). Piano chord data base and Standard music IOWA data base have been used for performance evaluation of the proposed scheme. Simulation results show that percentage error in pitch (described by the fundamental frequency) is minimum for the proposed i.e. MMW-based method.
Highlights
Pitch estimation is one of the most widely investigated areas of Music Signal Processing
This paper introduces a reasonably straightforward and computationally efficient fundamental frequency (F0) estimator for polyphonic music signals
The salient points are concluding from wavelet based multipitch estimation in polyphonic music as the work described for pitch estimation in polyphonic signals which can be used for monophonic signals too
Summary
Pitch estimation is one of the most widely investigated areas of Music Signal Processing. Pitch finding in monophonic music signals is simple though harmonics of fundamental frequencies are present with low amplitude in the time domain. Yeh et al [1] presented a frame based system for estimating the multiple fundamental frequencies of music signal based on short time fourier transform (STFT). A method for estimating the fundamental frequencies of several concurrent sounds in polyphonic music and multiple-speaker speech signals is presented in [2]. Many of the available methods for music transcription are based on extraction of the fundamental frequencies using frequency analysis tools such as Fast Fourier transform (FFT) [10, 11, 12]. A Complex continuous wavelet transform (CCWT) estimates the fundamental frequencies in single-channel polyphonic signals were proposed in [23].
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