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Improving melody extraction using Probabilistic Latent Component Analysis

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2 Author(s)
Jinyu Han ; Northwestern Univ., Evanston, IL, USA ; Ching-Wei Chen

We propose a new approach for automatic melody extraction from polyphonic audio, based on Probabilistic Latent Component Analysis (PLCA). An audio signal is first divided into vocal and non-vocal segments using a trained Gaussian Mixture Model (GMM) classifier. A statistical model of the non-vocal segments of the signal is then learned adaptively from this particular input music by PLCA. This model is then employed to remove the accompaniment from the mixture, leaving mainly the vocal components. The melody line is extracted from the vocal components using an auto-correlation algorithm. Quantitative evaluation shows that the new system performs significantly better than two existing melody extraction algorithms for polyphonic single-channel mixtures.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on

Date of Conference:

22-27 May 2011

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