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Ph.D. dissertation announcement: Sound Source Segregation

Dear auditory list members:

I would like to bring to your attention my recently completed Ph.D. dissertation, entitled "Auditory-based algorithms for sound segregation in multisource and reverberant environments".

An electronic version of the thesis is available at:


Please find the abstract below.

Sincerely, Nicoleta Roman

At a cocktail party, we can selectively attend to a single voice and filter out other interferences. This perceptual ability has motivated a new field of study known as computational auditory scene analysis (CASA) which aims to build speech separation systems that incorporate auditory principles. The psychological process of figure-ground segregation suggests that the target signal should be segregated as foreground while the remaining stimuli are treated as background. Accordingly, the computational goal of CASA should be to estimate an ideal time-frequency (T-F) binary mask, which selects the target if it is stronger than the interference in a local T-F unit. This dissertation investigates four aspects of CASA processing: location-based speech segregation, binaural tracking of multiple moving sources, binaural sound segregation in reverberation, and monaural segregation of reverberant speech. For localization, the auditory system utilizes the interaural time difference (ITD) and interaural intensity difference (IID) between the ears. We observe that within a narrow frequency band, modifications to the relative strength of the target source with respect to the interference trigger systematic changes for ITD and IID resulting in a characteristic clustering. Consequently, we propose a supervised learning approach to estimate the ideal binary mask. A systematic evaluation shows that the resulting system produces masks very close to the ideal binary ones and large speech intelligibility improvements. In realistic environments, source motion requires consideration. Binaural cues are strongly correlated with locations in T-F units dominated by one source resulting in channel-dependent conditional probabilities. Consequently, we propose a multi-channel integration method of these probabilities in order to compute the likelihood function in a target space. Finally, a hidden Markov model is employed for forming continuous tracks and automatically detecting the number of active sources. Reverberation affects the ITD and IID cues. We therefore propose a binaural segregation system that combines target cancellation through adaptive filtering and a binary decision rule to estimate the ideal binary mask. A major advantage of the proposed system is that it imposes no restrictions on the interfering sources. Quantitative evaluations show that our system outperforms related beamforming approaches. Psychoacoustic evidence suggests that monaural processing play a vital role in segregation. It is known that reverberation smears the harmonicity of speech signals. We therefore propose a two-stage separation system that combines inverse filtering of target room impulse response with pitch-based segregation. As a result of the first stage, the harmonicity of a signal arriving from target direction is partially restored while signals arriving from other locations are further smeared, and this leads to improved segregation and considerable signal-to-noise ratio gains.