Study of automatic melody extraction methods for Philippine indigenous music

In this study, we compared two methods for extracting the melody pitch from select Philippine indigenous music. Pitch is expressed as the fundamental frequency of the main melodic voice or lead instrument of a music sample. Our implementation of automatic melody extraction involves blind source separation and pitch detection. For blind source separation, we implemented the Harmonic-Percussive Source Separation (HPSS) algorithm and the Shifted Non-negative Matrix Factorization (SNMF) algorithm. The HPSS algorithm identifies the harmonic component from the prominent peaks in the spectrogram of a signal while the SNMF algorithm use timbre as criterion. The harmonic component is used to estimate the melody pitch. The HPSS and SNMF source separation algorithms are complemented with salience-based and data driven pitch detection algorithms, respectively. The two systems are evaluated using ten samples of Philippine indigenous music. After source separation, the estimated harmonic and percussive tracks were evaluated through subjective listening tests. Results from subjective tests show that SNMF perform better than HPSS for harmonic and percussive source separation. Moreover, objective tests using standard metrics indicate that the salience-based approach has higher accuracy in identifying the melody than the data driven approach.