[email protected]

PH.D. THESIS TOPIC

Neuro-Computational Basis of Sound Object Recognition

• Developed novel feature extraction methods which are extremely robust to noise for both speech and speaker identification tasks.
  • These features capture speech specific regions in the modulation domain which maximize reliability.
  • Multi-stream approach to further divide this region into subparts performs better in various noise conditions.
• Successfully demonstrated models for timbre which capture the perceptual space of musical instruments.
  • Attentional mechanisms in this perceptual space have been developed which can further boost the representation of any given target object.
  • Developed methods to adapt feature extraction and modeling stages to out-of-domain data.

Other Projects

Derived-STRF(Spectro-Temporal Receptive Field) contours for Speech recognition

• Developed an algorithm to learn STRFs from speech data to give sustained response
• Successfully used the resulting contour to derive robust features which show improved performance in noisy conditions

Phoneme recognition framework using STRFs

• Developed a mechanism to automatically select STRF features for each broad phoneme class
• Combined posteriors from multi-layered perceptrons trained on these features give improved performance.

Multi-resolution Analysis for Lung sounds

• Successfully extracted multidimensional features from lung sounds that were able to predict presence of abnormalities.

Speech based filter banks

• Derived filter banks from average spectrum of speech that were compared with perception based mel-like filter banks.

CV

Other Interests

Rock Climbing, Tennis, Racquet Ball, Hiking, Kayaking.

2010

A Phoneme Recognition Framework based on Auditory Spectro-Temporal Receptive Fields
Samuel Thomas, Kailash Patil, Sriram Ganapathy, Nima Mesgarani and Hynek Hermansky
Interspeech – 2010

© Kailash Patil, 2015. All rights reserved.