Event

Ph.D. Dissertation Defense: Lakshmi Krishnan

Monday, October 5, 2015
2:00 p.m.
AVW 2460
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: Ph.D. Dissertation Defense

Location: AVW 2460

Title: Mechanisms behind sound source segregation

Abstract:

While humans can easily segregate and track a speaker's voice in a loud noisy

environment, most modern speech recognition systems still perform poorly in loud

background noise. The computational principles behind auditory source segregation

in humans are not yet fully understood. In this dissertation, we develop a

computational model for source segregation inspired by auditory processing in the

brain. To support the key principles behind the computational model, we conduct a

series of electro-encephalography experiments using both simple tone-based

stimuli and more natural speech stimulus.

Most source segregation algorithms utilize some form of prior information about

the target speaker or use more than one simultaneous recording of the noisy speech

mixtures. Other methods develop models on the noise characteristics. Source

segregation of simultaneous speech mixtures with a single microphone recording

and no knowledge of the target speaker is still a challenge. Using the principle of

temporal coherence, we develop a novel computational model that exploits the

differences in the temporal evolution of the features that belong to different sources,

to perform unsupervised monaural source segregation. While using no prior

information about the target speaker, this method can gracefully incorporate

knowledge about the target source, if available, to further enhance the segregation.

Using this model, we are able to segregate speech from speech in noise, speech in

music sand speech mixtures. Aside from its unusual structure and computational

innovations, the proposed model provides testable hypotheses of the physiological

mechanisms of the remarkable perceptual ability of humans to segregate acoustic

sources, and of its psychophysical manifestations in navigating complex sensory

environments.

Next, through a series of EEG experiments we collect neurological evidence to

support the principle behind the model. We conduct EEG experiments to contrast

the neural signatures of synchronous and alternating tone sequences and study the

influence of attention on the measured neural response. Finally, we build an online

decoder in sensor space to decode auditory attention from EEG. Results from EEG

experiments provide further insights into the assumptions behind the

computational model and provide motivation for future single unit studies that can

provide more direct evidence for the principle of temporal coherence.

Audience: Graduate  Faculty