A biologically oriented algorithm for spatial sound segregation
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 as...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1004071/full |
| _version_ | 1811339028085080064 |
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| author | Kenny F. Chou Alexander D. Boyd Virginia Best H. Steven Colburn Kamal Sen |
| author_facet | Kenny F. Chou Alexander D. Boyd Virginia Best H. Steven Colburn Kamal Sen |
| author_sort | Kenny F. Chou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T18:19:12Z |
| format | Article |
| id | doaj.art-c9e3743b44cf48dea0f02bb416914db2 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-13T18:19:12Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-c9e3743b44cf48dea0f02bb416914db22022-12-22T02:35:32ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-10-011610.3389/fnins.2022.10040711004071A biologically oriented algorithm for spatial sound segregationKenny F. Chou0Alexander D. Boyd1Virginia Best2H. Steven Colburn3Kamal Sen4Department of Biomedical Engineering, Boston University, Boston, MA, United StatesDepartment of Biomedical Engineering, Boston University, Boston, MA, United StatesDepartment of Speech, Language and Hearing Sciences, Boston University, Boston, MA, United StatesDepartment of Biomedical Engineering, Boston University, Boston, MA, United StatesDepartment of Biomedical Engineering, Boston University, Boston, MA, United StatesListening 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.https://www.frontiersin.org/articles/10.3389/fnins.2022.1004071/fullmultitalker speech perceptionsound (audio) processingsound segregationcocktail party problembinaural hearingspatial listening |
| spellingShingle | Kenny F. Chou Alexander D. Boyd Virginia Best H. Steven Colburn Kamal Sen A biologically oriented algorithm for spatial sound segregation Frontiers in Neuroscience multitalker speech perception sound (audio) processing sound segregation cocktail party problem binaural hearing spatial listening |
| title | A biologically oriented algorithm for spatial sound segregation |
| title_full | A biologically oriented algorithm for spatial sound segregation |
| title_fullStr | A biologically oriented algorithm for spatial sound segregation |
| title_full_unstemmed | A biologically oriented algorithm for spatial sound segregation |
| title_short | A biologically oriented algorithm for spatial sound segregation |
| title_sort | biologically oriented algorithm for spatial sound segregation |
| topic | multitalker speech perception sound (audio) processing sound segregation cocktail party problem binaural hearing spatial listening |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1004071/full |
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