A deep learning method for grid-free localization and quantification of sound sources.
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| Title: | A deep learning method for grid-free localization and quantification of sound sources. |
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| Authors: | Kujawski, Adam1 (AUTHOR) adam.kujawski@tu-berlin.de, Herold, Gert1 (AUTHOR), Sarradj, Ennes1 (AUTHOR) |
| Source: | Journal of the Acoustical Society of America. Sep2019, Vol. 146 Issue 3, pEL225-EL231. 7p. |
| Subjects: | Acoustic localization, Image recognition (Computer vision), Microphone arrays, Deep learning |
| Abstract: | In this contribution it is examined whether the use of deep neural networks can lead to an accurate characterization of single point sources from microphone array data. Based on conventional beamforming maps, the proposed method aims at estimating the source coordinates and the strength. The residual network architecture, a well-established model in the field of image recognition, is successfully applied to this task. The investigation reveals a method that fast and accurately renders the position and strength of an unknown source. Moreover, the accuracy of the position estimation is higher than the grid resolution of the beamforming map. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 138991982 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A deep learning method for grid-free localization and quantification of sound sources. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kujawski%2C+Adam%22">Kujawski, Adam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> adam.kujawski@tu-berlin.de</i><br /><searchLink fieldCode="AR" term="%22Herold%2C+Gert%22">Herold, Gert</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sarradj%2C+Ennes%22">Sarradj, Ennes</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Acoustical+Society+of+America%22">Journal of the Acoustical Society of America</searchLink>. Sep2019, Vol. 146 Issue 3, pEL225-EL231. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Acoustic+localization%22">Acoustic localization</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Microphone+arrays%22">Microphone arrays</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this contribution it is examined whether the use of deep neural networks can lead to an accurate characterization of single point sources from microphone array data. Based on conventional beamforming maps, the proposed method aims at estimating the source coordinates and the strength. The residual network architecture, a well-established model in the field of image recognition, is successfully applied to this task. The investigation reveals a method that fast and accurately renders the position and strength of an unknown source. Moreover, the accuracy of the position estimation is higher than the grid resolution of the beamforming map. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=138991982 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1121/1.5126020 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: EL225 Subjects: – SubjectFull: Acoustic localization Type: general – SubjectFull: Image recognition (Computer vision) Type: general – SubjectFull: Microphone arrays Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: A deep learning method for grid-free localization and quantification of sound sources. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kujawski, Adam – PersonEntity: Name: NameFull: Herold, Gert – PersonEntity: Name: NameFull: Sarradj, Ennes IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 00014966 Numbering: – Type: volume Value: 146 – Type: issue Value: 3 Titles: – TitleFull: Journal of the Acoustical Society of America Type: main |
| ResultId | 1 |
