Blind Source Separation: New Proof of Bounded Component Analysis and Nonnegative Matrix Factorization Algorithms for Monaural Audio

Holly Gao

Research output: ThesisDoctoral Thesis

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Abstract

This PhD thesis presents a proof of the contrast function for bounded component analysis (BCA), and discusses one of the limitations of BCA. Additionally, this thesis proposes a blind source separation (BSS) algorithm, inspired by BCA and second­order statistics (SOS). For unsupervised monaural blind source separation (UMBSS), perceptual features are incorporated into nonnegative matrix factorization (NMF) to establish a family of algorithms. Moreover, a speech production model is combined with NMF to form a Tri-lSNMF algorithm, which decomposes the audio sources into three factors and enforces a harmonic structure and the continuity constraint on the three factors.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Supervisors/Advisors
  • Togneri, Roberto, Supervisor
  • Sreeram, Victor, Supervisor
Thesis sponsors
Award date23 Jul 2020
DOIs
Publication statusUnpublished - 2020

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