# Auditory Scene Analysis with Complex Sounds

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $373,418

## Abstract

PROJECT SUMMARY / ABSTRACT
Perhaps the most pervasive problem faced by listeners with hearing impairment or cochlear implants is the
difficulty of recognizing speech and other sounds in the presence of competing sound sources, as when
conversing at a restaurant. This difficulty in “sound segregation” – hearing a particular sound of interest when it
is embedded in a mixture of other sounds – often leads to frustration and social isolation, and is not adequately
addressed by current hearing aids and implants. Sound segregation difficulties are also commonly reported in
developmental auditory disorders. The long-term goal of the proposed research is to reveal the basis of sound
segregation and to provide insights that will facilitate improved prosthetic devices and remediation strategies,
as well as more effective machine systems for processing sounds, e.g. for automatic speech recognition. The
development of more effective devices, technologies, and therapies is currently limited by an incomplete
understanding of the factors that underlie sound segregation by normal-hearing listeners. In particular, little is
known about sound segregation with complex naturalistic sounds, in part because much of the research in this
area has been conducted using simple signals that are impoverished relative to the sounds listeners normally
encounter. We propose to enrich the understanding of sound segregation with three sets of experiments that
use novel sound synthesis methods to manipulate properties of natural speech and other sounds and test their
role in segregation with behavioral experiments in human listeners. Aim 1 manipulates the classically proposed
grouping cue provided by harmonic frequency relations and investigates the mechanisms underlying their
effect. Aim 2 investigates the role of prior knowledge of voice and speech structure on segregation, and should
help to explain why some voices are easier or harder to segregate than others. Aim 3 investigates the role of
attentive tracking in the segregation of sounds from mixtures, and will explore the factors that facilitate tracking
or cause it to fail. The results will reveal the mechanisms underlying sound segregation by the healthy auditory
system, and will provide insights into the factors that limit auditory comprehension in the presence of multiple
sound sources, hopefully suggesting new strategies for signal enhancement, prosthetic devices, and
behavioral remediation.

## Key facts

- **NIH application ID:** 9996340
- **Project number:** 5R01DC014739-05
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Josh H McDermott
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $373,418
- **Award type:** 5
- **Project period:** 2016-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9996340

## Citation

> US National Institutes of Health, RePORTER application 9996340, Auditory Scene Analysis with Complex Sounds (5R01DC014739-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9996340. Licensed CC0.

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