Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking

Abstract

The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-means clustering to yield improvements in separation ability for underdetermined situations using a nonlinear microphone array. However, rather than test the blind source separation ability solely on reverberant conditions, this paper extends this to include a variety of simulated and real-world noisy environments. Results reported encouraging separation ability and improved perceptual quality of the separated sources for such adverse conditions. Not only does this establish this proposed methodology as a credible improvement to the system, but also implies further applicability in areas such as noise suppression in adverse acoustic environments.

Multiple moving speaker tracking via degenerate unmixing estimation technique and Cardinality Balanced Multi-target Multi-Bernoulli Filter (DUET-CBMeMBer)

“…The use of BSS for acoustic separation in real room environment was performed by Lee, Bell and Lambert in 1997 [1]. Since then, many other viable BSS solutions to the "cocktail party problem" have been proposed [2] [3] [4] [5] [6] [7] [8]. Most of the subjects discussed in these settings are non-moving sound sources.…”

Multiple sound source tracking and identification via degenerate unmixing estimation technique and cardinality balanced multi-target multi-bernoulli filter (DUET-CBMeMBer) with track management

“…The classic "cocktail party problem" has been widely researched and various methods of blind source separation systems have been proposed to solve it [1], [2] [3], [4], [5]. Most of these methods assume that the sound sources are not moving.…”

Complex-valued mixing matrix estimation for blind separation of acoustic mixtures

“…On the other hand, when the sources are sufficiently sparse, one can also use time-frequency masking [17,38,39,87,113] to extract the sources. This is achieved using…”