Audio processing applications that use short-time signal analysis techniques typically utilize fixed window duration single- or multi-resolution analyses. However, different real-world signal conditions such as polyphony and non-stationarity, manifested as musical accompaniment and pitch-modulations, respectively, in the context of music content analysis, require varying data window lengths for reliable processing. In this paper, we investigate the use of signal sparsity for adapting analysis window lengths. Adaptive-window analysis driven by different measures of sparsity applied to the local spectrum, such as kurtosis and Gini index, is evaluated and shown to be superior to fixed-window analysis in terms of sinusoid detection and frequency estimation for simulated and real signals. A window main-lobe matching method for sinusoid detection is also shown to be more robust to signal conditions such as polyphony and frequency modulation relative to other methods.

Melody extraction algorithms for single-channel polyphonic music typically rely on the salience of the lead melodic instrument, considered here to be the singing voice. However the simultaneous presence of one or more pitched instruments in the polyphony can cause such a predominant-F0 tracker to switch between tracking the pitch of the voice and that of an instrument of comparable strength, resulting in reduced voice-pitch detection accuracy. We propose a system that, in addition to biasing the salience measure in favor of singing voice characteristics, acknowledges that the voice may not dominate the polyphony at all instants and therefore tracks an additional pitch to better deal with the potential presence of locally dominant pitched accompaniment. A feature based on the temporal instability of voice harmonics is used to finally identify the voice pitch. The proposed system is evaluated on test data that is representative of polyphonic music with strong pitched accompaniment. Results show that the proposed system is indeed able to recover melodic information lost to its single-pitch tracking counterpart, and also outperforms another state-of-the-art melody extraction system designed for polyphonic music.

This paper demonstrates the superiority of energy-based features derived from the knowledge of predominant-pitch, for singing voice detection in polyphonic music over commonly used spectral features. However, such energy-based features tend to misclassify loud, pitched instruments. To provide robustness to such accompaniment we exploit the relative instability of the pitch contour of the singing voice by attenuating harmonic spectral content belonging to stable-pitch instruments, using sinusoidal modeling. The obtained feature shows high classification accuracy when applied to north Indian classical music data and is also found suitable for automatic detection of vocal-instrumental boundaries required for smoothing the frame-level classifier decisions. Index Terms: audio segmentation, voice detection 1.