Flexible processing of Complex Auditory Scenes using Attention
Abstract
Analyzing constant streams of big data arriving from sensor arrays in an energy efficient manner is a formidable challenge for artificial systems. The brain tackles this problem in an elegant manner using the mechanism of attention. The goal of this project is to design a brain inspired audio processing algorithm that leverages attention to process audio streams from multiple spatial locations in a selective, flexible, and energy efficient manner. Specifically, this project will integrate a biologically based bottom-up algorithm for sound segregation (BOSSA), with a model of auditory attention (AIM), to develop a powerful top-down algorithm for audio processing in real-world noisy scenes with multiple sound sources. Compared to current alternatives, e.g., beamformers and deep neural networks, this algorithm will enable implementations that are more energy efficient and have a compact form factor. The algorithm will transform audio processing in complex scenes with multiple auditory sources, a challenging condition for both humans, e.g., those with hearing impairment, ADHD, or autism; as well as artificial speech recognition algorithms (e.g., SIRI or Alexa). Potential defense applications include audio surveillance and human machine communication in noisy scenes.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 08, 2024
- Source ID
- N000142412449
Entities
People
- Kamal Sen
Organizations
- Boston University
- Office of Naval Research
- United States Navy