TY - BOOK
T1 - Blind Source Separation: New Proof of Bounded Component Analysis and Nonnegative Matrix Factorization Algorithms for Monaural Audio
AU - Gao, Holly
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - blind source separation
KW - speech production model
KW - bounded component analysis
KW - HARMONIC STRUCTURE
KW - unsupervised monaural blind source separation
KW - multiplicative update rule
KW - nonnegative matrix factorization
U2 - 10.26182/5f3b7ca5bba0b
DO - 10.26182/5f3b7ca5bba0b
M3 - Doctoral Thesis
ER -