A Comparison and Combination of Unsupervised Blind Source Separation Techniques

Konferenz: Speech Communication - 14th ITG Conference
29.09.2021 - 01.10.2021 in online

Tagungsband: ITG-Fb. 298: Speech Communication

Seiten: 5Sprache: EnglischTyp: PDF

Persönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt

Autoren:
Boeddeker, Christoph; Rautenberg, Frederik; Haeb-Umbach, Reinhold (Paderborn University, Department of Communications Engineering, Paderborn, Germany)

Inhalt:
Unsupervised blind source separation methods do not require a training phase and thus cannot suffer from a train-test mismatch, which is a common concern in neural network based source separation. The unsupervised techniques can be categorized in two classes, those building upon the sparsity of speech in the Short-Time Fourier transform domain and those exploiting non- Gaussianity or non-stationarity of the source signals. In this contribution, spatial mixture models which fall in the first category and independent vector analysis (IVA) as a representative of the second category are compared w.r.t. their separation performance and the performance of a downstream speech recognizer on a reverberant dataset of reasonable size. Furthermore, we introduce a serial concatenation of the two, where the result of the mixture model serves as initialization of IVA, which achieves significantly better WER performance than each algorithm individually and even approaches the performance of a much more complex neural network based technique.