Listening in an acoustically cluttered scene remains a difficult task for both machines and hearing-impaired listeners. Normal-hearing listeners accomplish this task with relative ease by segregating the scene into its constituent sound sources, then selecting and attending to a target source. An assistive listening device that mimics the biological mechanisms underlying this behavior may provide an effective solution for those with difficulty listening in acoustically cluttered environments (e.g., a cocktail party). Here, we present a binaural sound segregation algorithm based on a hierarchical network model of the auditory system. In the algorithm, binaural sound inputs first drive populations of neurons tuned to specific spatial locations and frequencies. The spiking response of neurons in the output layer are then reconstructed into audible waveforms via a novel reconstruction method. We evaluate the performance of the algorithm with a speech-on-speech intelligibility task in normal-hearing listeners. This two-microphone-input algorithm is shown to provide listeners with perceptual benefit similar to that of a 16-microphone acoustic beamformer. These results demonstrate the promise of this biologically inspired algorithm for enhancing selective listening in challenging multi-talker scenes.
Keywords: binaural hearing; cocktail party problem; hearing loss; multitalker speech perception; sound (audio) processing; sound segregation; spatial listening.
Copyright © 2022 Chou, Boyd, Best, Colburn and Sen.