Share and Export
Share and Export
O'Grady, Paul D. and Pearlmutter, Barak A. (2007) Discovering Convolutive Speech Phones Using Sparseness and Non-negativity. In: ICA 2007: Independent Component Analysis and Signal Separation. Lecture Notes in Computer Science book series (LNCS) (4666). Springer, pp. 520-527. ISBN 9783540744948
Preview
BP-Discovering-2007.pdf
Download (467kB) | Preview
Abstract
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. Here, we present a convolutive NMF algorithm that includes a sparseness constraint on the activations and has multiplicative updates. In combination with a spectral magnitude transform of speech, this method extracts speech phones that exhibit sparse activation patterns, which we use in a supervised separation scheme for monophonic mixtures.
| Item Type: | Book Section |
|---|---|
| Additional Information: | Cite as: O’Grady P.D., Pearlmutter B.A. (2007) Discovering Convolutive Speech Phones Using Sparseness and Non-negativity. In: Davies M.E., James C.J., Abdallah S.A., Plumbley M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg |
| Keywords: | Separation Performance; Positive Matrix Factorization; Sparseness Constraint; Female Speaker; Reconstruction Objective; |
| Academic Unit: | Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 10244 |
| Identification Number: | 10.1007/978-3-540-74494-8_65 |
| Depositing User: | Barak Pearlmutter |
| Date Deposited: | 27 Nov 2018 16:54 |
| Publisher: | Springer |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/10244 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
- Item control page
Downloads
Downloads per month over past year
