A Classification-based Cocktail-party Processor

At a cocktail party, a listener can selectively attend to a single voice and filter out other acoustical interferences. How to simulate this perceptual ability remains a great challenge. This paper describes a novel supervised learning approach to speech segregation, in which a target speech signal is separated from interfering sounds using spatial location cues: interaural time differences (ITD) and interaural intensity differences (IID). Motivated by the auditory masking effect, we employ the notion of an ideal time-frequency binary mask, which selects the target if it is stronger than the interference in a local time-frequency unit. Within a narrow frequency band, modifications to the relative strength of the target source with respect to the interference trigger systematic changes for estimated ITD and IID. For a given spatial configuration, this interaction produces characteristic clustering in the binaural feature space. Consequently, we perform pattern classification in order t...

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Nicoleta Roman, DeLiang L. Wang, Guy J. Brown
• Keywords: Binary Masks, NIPS 2003, NIPS 2007, Target Speech Signal, Time-frequency Binary Mask