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Ranjani, HG and Sreenivas, TV (2015) MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM. In: 40th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), APR 19-24, 2014, Brisbane, AUSTRALIA, pp. 191-195.

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We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.

Item Type: Conference Proceedings
Series.: International Conference on Acoustics Speech and Signal Processing ICASSP
Publisher: IEEE
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords: Factorial HMM; Latent Variable; Polyphony; F-GM-HMM
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 17 Feb 2016 06:11
Last Modified: 17 Feb 2016 06:11
URI: http://eprints.iisc.ac.in/id/eprint/53292

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