Phase difference and tensor factorization models for audio source separation

Traa, Johannes

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https://hdl.handle.net/2142/95277 Copy

Description

Title

Phase difference and tensor factorization models for audio source separation

Author(s)

Traa, Johannes

Issue Date

2016-10-10

Director of Research (if dissertation) or Advisor (if thesis)

Smaragdis, Paris

Doctoral Committee Chair(s)

Smaragdis, Paris

Committee Member(s)

• Hasegawa-Johnson, Mark
• Bresler, Yoram
• Stein, Noah

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Nonnegative matrix factorization
• Nonnegative tensor factorization
• Interchannel phase differences
• Audio Source Separation

Abstract

Audio source separation is a well-known problem in the speech community. Many methods have been proposed to isolate speech signals from a multichannel mixture. In this thesis, we will explore a number of techniques involving interchannel phase difference (IPD) features within a tensor factorization framework. IPD features can be extracted on a time-frequency (TF) grid and are a function of the phase characteristics of the mixing process. Thus, the ultimate goal is to form a clustering of these features and produce TF masks that can be used to perform the separation. We discuss various non-tensor-based methods that are capable of modeling linear and nonlinear IPD trends. Then, we discuss generalizations to both nonnegative and complex tensor factorizations (NTF, CTF). We show that each method performs best in certain circumstances and we conclude by saying that more work is needed to devise a generally superior approach.

Graduation Semester

2016-12

Type of Resource

text

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http://hdl.handle.net/2142/95277

Copyright and License Information

Copyright 2016 Johannes Traa

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