Advancements in the Time-Frequency Approach to Multichannel Blind Source Separation

The ability of the human cognitive system to distinguish between multiple, simultaneously active sources of sound is a remarkable quality that is often taken for granted. This capability has been studied extensively within the speech processing community andmany an endeavor at imitation has beenmade. However, automatic speech processing systems are yet to perform at a level akin to human proficiency (Lippmann, 1997) and are thus frequently faced with the quintessential "cocktail party problem": the inadequacy in the processing of the target speaker/s when there are multiple speakers in the scene (Cherry, 1953). The implementation of a source separation algorithm can improve the performance of such systems. Source separation is the recovery of the original sources from a set of observations; if no a priori information of the original sources and/or mixing process is available, it is termed blind source separation (BSS). Rather than rely on the availability of a priori information of the acoustic scene, BSS methods often employ an assumption on the constituent source signals, and/or an exploitation of the spatial diversity obtained through a microphone array. BSS has many important applications in both the audio and biosignal disciplines, including medical imaging and communication systems.