Semi-Supervised Polyphonic Source Identification using PLCA Based Graph Clustering


For identifying instruments or singers in the polyphonic audio, supervised probabilistic latent component analysis (PLCA) is a popular tool. But in many cases individual source audio is not available for training. To address this problem, this paper proposes a novel scheme using semi-supervised PLCA with probabilistic graph clustering, which does not require individual sources for training. The PLCA is based on source-filter approach which models the spectral envelope as a weighted sum of elementary band-pass filters. The novel graph based approach, embedded in the PLCA framework, takes into account various perceptual cues for characterizing a source. These cues include temporal cues like the evolution of F0 contours as well as the acoustic cues like mel-frequency cepstral coefficients. The proposed scheme shows better results in identifying vocal sources than a state of the art unsupervised scheme. In addition , the proposed framework can be used to incorporate perceptual cues so as to enhance the performance of supervised schemes too.

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