NDLI: Linear Regression on Sparse Features for Single-Channel Speech Separation

Content Provider	IEEE Xplore Digital Library
Author	Schmidt, Mikkel N. Olsson, Rasmus K.
Copyright Year	2007
Description	Author affiliation: Technical University of Denmark, Richard Petersens Plads, Bldg. 321, DK-2800 Kgs. Lyngby, Denmark. Email: mns@imm.dtu.dk (Schmidt, Mikkel N.) \|\| Technical University of Denmark, Richard Petersens Plads, Bldg. 321, DK-2800 Kgs. Lyngby, Denmark. Email: rko@imm.dtu.dk (Olsson, Rasmus K.)
Abstract	In this work we address the problem of separating multiple speakers from a single microphone recording. We formulate a linear regression model for estimating each speaker based on features derived from the mixture. The employed feature representation is a sparse, non-negative encoding of the speech mixture in terms of pre-learned speaker-dependent dictionaries. Previous work has shown that this feature representation by itself provides some degree of separation. We show that the performance is significantly improved when regression analysis is performed on the sparse, non-negative features, both compared to linear regression on spectral features and compared to separation based directly on the non-negative sparse features.
Starting Page	26
Ending Page	29
File Size	153119
Page Count	4
File Format	PDF
ISBN	9781424416189
DOI	10.1109/ASPAA.2007.4393010
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear regression Dictionaries Hidden Markov models Encoding Speech analysis Sparse matrices Humans Microphones Auditory system Training data
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Deep neural networks for single channel source separation

Learning a hierarchical dictionary for single-channel speech separation

Single-Channel Source Separation Using EMD-Subband Variable Regularized Sparse Features

Accurate hidden Markov models for non-audible murmur (NAM) recognition based on iterative supervised adaptation

Exemplar-based Sparse Representation phone identification features

Improving environmental robustness in large vocabulary speech recognition

A Novel Low Complexity VQ-Based Single Channel Speech Separation Technique

Linear Program Differentiation for Single-Channel Speech Separation

Lowresource speech recognition with automatically learned sparse inverse covariance matrices

Linear Regression on Sparse Features for Single-Channel Speech Separation

Similar Documents

Deep neural networks for single channel source separation

Learning a hierarchical dictionary for single-channel speech separation

Single-Channel Source Separation Using EMD-Subband Variable Regularized Sparse Features

Accurate hidden Markov models for non-audible murmur (NAM) recognition based on iterative supervised adaptation

Exemplar-based Sparse Representation phone identification features

Improving environmental robustness in large vocabulary speech recognition

A Novel Low Complexity VQ-Based Single Channel Speech Separation Technique

Linear Program Differentiation for Single-Channel Speech Separation

Lowresource speech recognition with automatically learned sparse inverse covariance matrices

Linear Regression on Sparse Features for Single-Channel Speech Separation