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Dirichlet latent variable model : a dynamic model based on Dirichlet prior for audio processing
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Kumar, Anurendra, Guha, Tanaya and Ghosh, Prasanta K. (2019) Dirichlet latent variable model : a dynamic model based on Dirichlet prior for audio processing. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27 (5). pp. 919-931. doi:10.1109/TASLP.2019.2903288 ISSN 2329-9290.
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Official URL: https://doi.org/10.1109/TASLP.2019.2903288
Abstract
We propose a dynamic latent variable model for learning latent bases from time varying, non-negative data. We take a probabilistic approach to modeling the temporal dependence in data by introducing a dynamic Dirichlet prior – a Dirichlet distribution with dynamic parameters. This new distribution allows us to assure non-negativity and avoid intractability when sequential updates are performed (otherwise encountered in using Dirichlet prior). We refer to the proposed model as the Dirichlet latent variable model (DLVM). We develop an expectation maximization algorithm for the proposed model, and also derive a maximum a posteriori estimate of the parameters. Furthermore, we connect the proposed DLVM to two popular latent basis learning methods - probabilistic latent component analysis (PLCA) and non-negative matrix factorization (NMF).We show that (i) PLCA is a special case of our DLVM, and (ii) DLVM can be interpreted as a dynamic version of NMF. The usefulness of DLVM is demonstrated for three audio processing applications - speaker source separation, denoising, and bandwidth expansion. To this end, a new algorithm for source separation is also proposed. Through extensive experiments on benchmark databases, we show that the proposed model out performs several relevant existing methods in all three applications.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Latent variables, Algorithms, Signal processing | ||||||||
| Journal or Publication Title: | IEEE/ACM Transactions on Audio, Speech, and Language Processing | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 2329-9290 | ||||||||
| Official Date: | 1 May 2019 | ||||||||
| Dates: |
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| Volume: | 27 | ||||||||
| Number: | 5 | ||||||||
| Page Range: | pp. 919-931 | ||||||||
| DOI: | 10.1109/TASLP.2019.2903288 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 5 March 2019 | ||||||||
| Date of first compliant Open Access: | 6 March 2019 | ||||||||
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