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 semisupervised PLCAwith 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.

Extracted Key Phrases

4 Figures and Tables

Cite this paper

@inproceedings{Arora2013SemiSupervisedPS, title={Semi-Supervised Polyphonic Source Identification using PLCA Based Graph Clustering}, author={Vipul Arora and Laxmidhar Behera}, booktitle={ISMIR}, year={2013} }