Vishweshwara Rao

Vocal Melody Extraction in the Presence of Pitched Accompaniment in Polyphonic Music

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.

A melody detection user interface for polyphonic music

The automatic extraction of the melody of the music from polyphonic recordings is a challenging problem for which no general solutions currently exist. We present a novel interface for semi-automatic melody extraction with the goal to provide highly accurate pitch tracks of the lead voice with minimal user intervention. Audio-visual feedback facilitates the validation of the obtained melodic contour and user control of the analysis parameters enables direct and effective control over the voice pitch detection module by the intelligent user. This paper describes the interface and discusses the features in which it differs from more conventional audio analyses interfaces.

Context-Aware features for singing voice detection in polyphonic music

The effectiveness of audio content analysis for music retrieval may be enhanced by the use of available metadata. In the present work, observed differences in singing style and instrumentation across genres are used to adapt acoustic features for the singing voice detection task. Timbral descriptors traditionally used to discriminate singing voice from accompanying instruments are complemented by new features representing the temporal dynamics of source pitch and timbre. A method to isolate the dominant source spectrum serves to increase the robustness of the extracted features in the context of polyphonic audio. While demonstrating the effectiveness of combining static and dynamic features, experiments on a culturally diverse music database clearly indicate the value of adapting feature sets to genre-specific acoustic characteristics. Thus commonly available metadata, such as genre, can be useful in the front-end of an MIR system.

Meter detection from audio for indian music

The meter of a musical excerpt provides high-level rhythmic information and is valuable in many music information retrieval tasks. We investigate the use of a computationally efficient approach to metrical analysis based on psycho-acoustically motivated decomposition of the audio signal. A two-stage comb filter-based approach, originally proposed for double/ triple meter estimation, is extended to a septuple meter (such as 7/8 time-signature) and its performance evaluated on a sizable Indian music database. We find that this system works well for Indian music and the distribution of musical stress/accents across a temporal grid can be utilized to obtain the metrical structure of audio automatically.

Signal-Driven Window-Length Adaptation for Sinusoid Detection in Polyphonic Music

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.