Machine Learning in Acoustics: A Review and Open-source Repository
Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (ML) in acoustics including deep learning (DL). Using the Pytho...
Saved in:
| Published in | NPJ Acoustics Vol. 1; no. 1; p. 18 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
09.09.2025
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 3005-141X 3005-141X |
| DOI | 10.1038/s44384-025-00021-w |
Cover
| Abstract | Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (ML) in acoustics including deep learning (DL). Using the Python high-level programming language, we demonstrate a broad collection of ML techniques to detect and find patterns for classification, regression, and generation in acoustics data automatically. We have ML examples including acoustic data classification, generative modeling for spatial audio, and physics-informed neural networks. This work includes
AcousticsML
, a set of practical Jupyter notebook examples on GitHub demonstrating ML benefits and encouraging researchers and practitioners to apply reproducible data-driven approaches to acoustic challenges. |
|---|---|
| AbstractList | Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (ML) in acoustics including deep learning (DL). Using the Python high-level programming language, we demonstrate a broad collection of ML techniques to detect and find patterns for classification, regression, and generation in acoustics data automatically. We have ML examples including acoustic data classification, generative modeling for spatial audio, and physics-informed neural networks. This work includes AcousticsML, a set of practical Jupyter notebook examples on GitHub demonstrating ML benefits and encouraging researchers and practitioners to apply reproducible data-driven approaches to acoustic challenges. Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (ML) in acoustics including deep learning (DL). Using the Python high-level programming language, we demonstrate a broad collection of ML techniques to detect and find patterns for classification, regression, and generation in acoustics data automatically. We have ML examples including acoustic data classification, generative modeling for spatial audio, and physics-informed neural networks. This work includes AcousticsML , a set of practical Jupyter notebook examples on GitHub demonstrating ML benefits and encouraging researchers and practitioners to apply reproducible data-driven approaches to acoustic challenges. |
| ArticleNumber | 18 |
| Author | Jenkins, William F. Zhang, You Gerstoft, Peter Verburg, Samuel A. McCarthy, Ryan A. |
| Author_xml | – sequence: 1 givenname: Ryan A. surname: McCarthy fullname: McCarthy, Ryan A. email: r1mccarthy@ucsd.edu organization: Scripps Institution of Oceanography, UC San Diego – sequence: 2 givenname: You surname: Zhang fullname: Zhang, You organization: Department of Electrical and Computer Engineering, University of Rochester – sequence: 3 givenname: Samuel A. surname: Verburg fullname: Verburg, Samuel A. organization: Department of Electrical and Photonics Engineering, Technical University of Denmark – sequence: 4 givenname: William F. surname: Jenkins fullname: Jenkins, William F. organization: Scripps Institution of Oceanography, UC San Diego – sequence: 5 givenname: Peter surname: Gerstoft fullname: Gerstoft, Peter organization: Scripps Institution of Oceanography, UC San Diego, Department of Electrical and Photonics Engineering, Technical University of Denmark |
| BookMark | eNp9kM1LAzEQxYNUsNb-A54WPEdn8rGbeKvFL6gURMFb2M1ma0SzNWld-t-7uoKePM0wvPfm8Tsko9AGR8gxwikCV2dJCK4EBSYpADCk3R4ZcwBJUeDT6M9-QKYp-QqkErpAJsbk4q60zz64bOHKGHxYZT5kM9tu08bbdJ7Nsnv34V2XlaHOlmsXaGq30br-vG6T37Rxd0T2m_I1uenPnJDHq8uH-Q1dLK9v57MFtYiyoznjhaprtAi51ZXIC8tkUdW6YVLmgFg4oRzYXFS51AVnqsSiYaJmCq3OBZ-QkyF3Hdv3rUsb89JXCf1Lw5nQDJTW0KvYoLKxTSm6xqyjfyvjziCYL1xmwGV6XOYbl-l6Ex9MqReHlYu_0f-4PgGXS208 |
| Cites_doi | 10.1121/10.0006783 10.1121/10.0020655 10.1038/s42254-021-00314-5 10.3390/electronics10192329 10.11583/DTU.25867705.v1 10.1121/1.4809678 10.1016/j.ecoinf.2021.101236 10.1109/ICASSP.2017.7952261 10.1109/TASLP.2023.3268730 10.1109/LGRS.2019.2909218 10.4208/cicp.OA-2020-0164 10.1109/TPAMI.2023.3261988 10.1109/JPROC.2015.2494218 10.1121/10.0003501 10.1121/10.0016896 10.1016/j.ecoinf.2023.102449 10.1007/s10915-022-01939-z 10.1121/2.0001383 10.1121/10.0035573 10.1121/10.0004221 10.1016/j.jcp.2021.110768 10.1121/1.1419086 10.1145/3292500.3330701 10.1109/ACCESS.2024.3506973 10.1007/s10462-022-10293-3 10.21437/Interspeech.2021-599 10.1121/10.0025543 10.1038/s41598-019-48909-4 10.1007/s11071-023-08933-6 10.1145/2939672.2939778 10.1016/j.ymssp.2023.110535 10.1007/978-3-662-45620-0 10.1109/ICASSP40776.2020.9053327 10.21437/Interspeech.2019-2821 10.1109/ICASSP39728.2021.9414750 10.1038/s41586-023-06221-2 10.1109/ICASSP49357.2023.10096813 10.1109/JOE.2023.3292417 10.1109/ICASSP49357.2023.10095801 10.1016/j.cageo.2021.104751 10.1121/10.0034707 10.1016/j.cma.2021.114474 10.1109/ICASSP40776.2020.9054580 10.1121/10.0035175 10.1007/s10409-021-01148-1 10.1121/1.5133944 10.1007/s10444-023-10065-9 10.1109/JSTSP.2019.2909077 10.1121/10.0011809 10.1109/ACCESS.2021.3087697 10.1109/ICASSP40776.2020.9053905 10.1111/1467-9868.00293 10.1145/3422622 10.1121/10.0028177 10.21437/Interspeech.2018-1113 10.1016/j.compgeo.2022.105223 10.21437/Interspeech.2024-1333 10.1121/10.0026231 10.21105/joss.00861 10.1007/s10921-020-00705-1 10.1145/3561048 10.1109/CVPR52688.2022.01042 10.1111/2041-210X.14103 10.1121/10.0024750 10.1109/ICASSP48485.2024.10446761 10.1785/0220200164 10.1109/OJSP.2025.3528330 10.1111/rssb.12377 10.21437/Interspeech.2021-1820 10.1109/ICASSP48485.2024.10448477 10.3390/s21237834 10.1016/j.jfranklin.2023.11.038 10.1007/s44295-023-00005-0 10.3390/s22083033 10.1098/rsta.2020.0093 10.1121/10.0034856 10.1109/WASPAA58266.2023.10248178 10.1016/j.cma.2021.113938 10.1109/ICASSP48485.2024.10446097 10.1109/ACCESS.2019.2938227 10.1109/OCEANS47191.2022.9977333 10.1137/20M1318043 10.1121/10.0025235 10.1016/j.cma.2024.116813 10.1121/1.423355 10.17743/jaes.2019.0024 10.1007/s10845-018-1456-1 10.1121/10.0026026 10.1121/10.0009057 10.1121/10.0016887 10.1121/10.0001461 10.1016/j.jcp.2018.10.045 10.1126/sciadv.1602614 10.1109/TVT.2021.3102302 10.1016/B978-0-12-801522-3.00012-4 10.1029/2021JB021716 10.1609/aaai.v32i1.11491 10.1109/JSTSP.2019.2917582 10.1016/j.ultras.2022.106872 10.1121/10.0036312 10.17743/jaes.2022.0066 10.1214/ss/1009213726 10.1109/WASPAA52581.2021.9632672 10.21437/Interspeech.2021-230 10.1016/j.cma.2019.112623 10.1109/MSP.2024.3465896 10.1121/10.0001731 10.1111/mice.12685 10.1109/ICASSP49660.2025.10889311 10.3390/axioms12100982 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025 The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2025 – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PTHSS |
| DOI | 10.1038/s44384-025-00021-w |
| DatabaseName | Springer Nature OA Free Journals CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Engineering Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection |
| DatabaseTitle | CrossRef Publicly Available Content Database Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Engineering Collection |
| DatabaseTitleList | Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| EISSN | 3005-141X |
| ExternalDocumentID | 10_1038_s44384_025_00021_w |
| GrantInformation_xml | – fundername: Office of Naval Research grantid: N00014-24-1-2401; N000142412016 funderid: https://doi.org/10.13039/100000006 |
| GroupedDBID | 0R~ AAJSJ AASML ALMA_UNASSIGNED_HOLDINGS C6C AAYXX CITATION 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO EBLON HCIFZ L6V M7S NAO PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PTHSS SNYQT |
| ID | FETCH-LOGICAL-c115w-62378dd1c106c9b467c257bd9f25560117e48e0c64b6597328a17f24d281c9643 |
| IEDL.DBID | AAJSJ |
| ISSN | 3005-141X |
| IngestDate | Fri Sep 12 01:43:04 EDT 2025 Wed Oct 01 05:28:02 EDT 2025 Wed Sep 10 01:46:06 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c115w-62378dd1c106c9b467c257bd9f25560117e48e0c64b6597328a17f24d281c9643 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://doi.org/10.1038%2Fs44384-025-00021-w |
| PQID | 3249208990 |
| PQPubID | 7343596 |
| ParticipantIDs | proquest_journals_3249208990 crossref_primary_10_1038_s44384_025_00021_w springer_journals_10_1038_s44384_025_00021_w |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20250909 |
| PublicationDateYYYYMMDD | 2025-09-09 |
| PublicationDate_xml | – month: 9 year: 2025 text: 20250909 day: 9 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London |
| PublicationTitle | NPJ Acoustics |
| PublicationTitleAbbrev | npj Acoust |
| PublicationYear | 2025 |
| Publisher | Nature Publishing Group UK Nature Publishing Group |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group |
| References | H Niu (21_CR11) 2023; 1 21_CR22 21_CR21 S Koyama (21_CR82) 2025; 41 A Vardi (21_CR107) 2024; 156 Y Tagawa (21_CR42) 2021; 10 ID Khurjekar (21_CR108) 2023; 154 X Li (21_CR127) 2020; 31 21_CR28 S Wang (21_CR91) 2021; 384 R Tibshirani (21_CR141) 2001; 63 21_CR24 R Mattey (21_CR144) 2022; 390 H Wang (21_CR146) 2023; 620 21_CR99 D Yang (21_CR31) 2023; 31 WF Jenkins (21_CR97) 2024; 156 21_CR93 21_CR90 SH Rudy (21_CR70) 2017; 3 D De Salvio (21_CR132) 2023; 153 SE Eskimez (21_CR51) 2019; 13 BA Tama (21_CR126) 2023; 56 L Sayigh (21_CR17) 2017; 27 S Wang (21_CR143) 2024; 421 21_CR18 PC Bermant (21_CR125) 2019; 9 21_CR16 21_CR15 21_CR14 MD Jedrusiak (21_CR134) 2024; 155 21_CR13 21_CR12 C-C Chien (21_CR140) 2023; 155 B Moseley (21_CR94) 2023; 49 H Wang (21_CR85) 2023; 128 PP Angelov (21_CR113) 2021; 11 21_CR80 C Gurbuz (21_CR48) 2021; 149 GE Karniadakis (21_CR7) 2021; 3 R Martinez-Cantin (21_CR98) 2014; 15 K Shukla (21_CR84) 2020; 39 D Snover (21_CR139) 2021; 92 21_CR149 L Breiman (21_CR101) 2001; 16 W Zhu (21_CR145) 2021; 151 21_CR147 A Fisher (21_CR117) 2019; 20 S Cuomo (21_CR75) 2022; 92 J Bergstra (21_CR95) 2012; 13 M Zhou (21_CR49) 2021; 70 S Cai (21_CR6) 2021; 37 E Fernandez-Grande (21_CR27) 2023; 153 S Kahl (21_CR122) 2021; 61 S Li (21_CR32) 2024; 155 C Zhang (21_CR137) 2025; 157 I Goodfellow (21_CR37) 2020; 63 MA Nabian (21_CR92) 2021; 36 S Wang (21_CR89) 2022; 449 S Wang (21_CR88) 2021; 43 21_CR79 21_CR131 21_CR66 21_CR65 N Xiang (21_CR106) 2020; 148 21_CR130 21_CR64 C Guezenoc (21_CR25) 2020; 147 21_CR63 21_CR62 DW Apley (21_CR118) 2020; 82 21_CR61 21_CR60 K Kashinath (21_CR76) 2021; 379 21_CR2 21_CR1 21_CR4 21_CR3 21_CR129 Y Liu (21_CR136) 2025; 157 21_CR69 21_CR123 21_CR68 21_CR120 21_CR67 21_CR121 21_CR55 21_CR53 F-A Croitoru (21_CR54) 2023; 45 W Zhai (21_CR73) 2023; 111 21_CR52 21_CR50 Z-H Michalopoulou (21_CR8) 2021; 150 SE Dosso (21_CR105) 2002; 111 S Yoon (21_CR78) 2024; 155 B Shahriari (21_CR96) 2016; 104 S Savović (21_CR86) 2023; 12 21_CR119 BZ Cunha (21_CR10) 2023; 200 21_CR115 P Gerstoft (21_CR102) 1998; 104 Y Wang (21_CR116) 2021; 22 21_CR59 21_CR111 21_CR58 P-A Grumiaux (21_CR9) 2022; 152 21_CR57 21_CR56 21_CR110 I Szöke (21_CR20) 2019; 13 21_CR44 21_CR43 RT Chen (21_CR72) 2018; 31 R Dwivedi (21_CR112) 2023; 55 L McInnes (21_CR114) 2018; 3 21_CR41 K Li (21_CR71) 2023; 48 21_CR40 F Brinkmann (21_CR23) 2019; 67 MJ Bianco (21_CR29) 2021; 9 SR Saufi (21_CR128) 2019; 7 J Bonnel (21_CR103) 2013; 134 M Raissi (21_CR74) 2019; 378 MJ Guerrero (21_CR133) 2023; 14 MJ Bianco (21_CR5) 2019; 146 X Karakonstantis (21_CR81) 2024; 155 M Olivieri (21_CR83) 2021; 21 21_CR109 K Gibb (21_CR135) 2024; 80 D Fantini (21_CR142) 2025; 6 21_CR104 M Blaszke (21_CR124) 2022; 22 21_CR100 21_CR47 21_CR46 SM Mousavi (21_CR138) 2019; 16 21_CR45 21_CR33 21_CR30 G Kissas (21_CR77) 2020; 358 R Liu (21_CR87) 2024; 155 P Gerstoft (21_CR148) 2024; 156 S Becker (21_CR19) 2024; 361 21_CR39 21_CR38 I Engel (21_CR26) 2023; 71 21_CR36 21_CR35 21_CR34 |
| References_xml | – volume: 150 start-page: 3204 year: 2021 ident: 21_CR8 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0006783 – volume: 154 start-page: 979 year: 2023 ident: 21_CR108 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0020655 – volume: 3 start-page: 422 year: 2021 ident: 21_CR7 publication-title: Nat. Rev. Phys. doi: 10.1038/s42254-021-00314-5 – volume: 10 start-page: 2329 year: 2021 ident: 21_CR42 publication-title: Electronics doi: 10.3390/electronics10192329 – ident: 21_CR24 – ident: 21_CR22 doi: 10.11583/DTU.25867705.v1 – ident: 21_CR149 – volume: 134 start-page: 120 year: 2013 ident: 21_CR103 publication-title: J. Acoust. Soc. Am. doi: 10.1121/1.4809678 – ident: 21_CR62 – volume: 61 start-page: 101236 year: 2021 ident: 21_CR122 publication-title: Ecol. Inform. doi: 10.1016/j.ecoinf.2021.101236 – ident: 21_CR3 – ident: 21_CR18 doi: 10.1109/ICASSP.2017.7952261 – volume: 31 start-page: 1720 year: 2023 ident: 21_CR31 publication-title: IEEE/ACM Trans. Audio, Speech, Lang. Proc. doi: 10.1109/TASLP.2023.3268730 – volume: 16 start-page: 1693 year: 2019 ident: 21_CR138 publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2019.2909218 – ident: 21_CR38 – ident: 21_CR53 – ident: 21_CR93 doi: 10.4208/cicp.OA-2020-0164 – volume: 13 start-page: 281 year: 2012 ident: 21_CR95 publication-title: J. Mach. Learn. Res. – ident: 21_CR15 – volume: 45 start-page: 10850 year: 2023 ident: 21_CR54 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2023.3261988 – ident: 21_CR100 – volume: 104 start-page: 148 year: 2016 ident: 21_CR96 publication-title: Proc. IEEE doi: 10.1109/JPROC.2015.2494218 – volume: 149 start-page: 1162 year: 2021 ident: 21_CR48 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0003501 – volume: 15 start-page: 3915 year: 2014 ident: 21_CR98 publication-title: J. Mach. Learn. Res. – ident: 21_CR129 – volume: 153 start-page: 1179 year: 2023 ident: 21_CR27 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0016896 – volume: 80 start-page: 102449 year: 2024 ident: 21_CR135 publication-title: Ecol. Inform. doi: 10.1016/j.ecoinf.2023.102449 – ident: 21_CR35 – ident: 21_CR56 – volume: 92 start-page: 88 year: 2022 ident: 21_CR75 publication-title: J. Sci. Comput. doi: 10.1007/s10915-022-01939-z – ident: 21_CR79 doi: 10.1121/2.0001383 – volume: 157 start-page: 669 year: 2025 ident: 21_CR137 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0035573 – ident: 21_CR130 doi: 10.1121/10.0004221 – ident: 21_CR147 – ident: 21_CR1 – volume: 449 start-page: 110768 year: 2022 ident: 21_CR89 publication-title: J. Computational Phys. doi: 10.1016/j.jcp.2021.110768 – volume: 111 start-page: 129 year: 2002 ident: 21_CR105 publication-title: J. Acoustical Soc. Am. doi: 10.1121/1.1419086 – ident: 21_CR99 doi: 10.1145/3292500.3330701 – ident: 21_CR44 doi: 10.1109/ACCESS.2024.3506973 – volume: 56 start-page: 4667 year: 2023 ident: 21_CR126 publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-022-10293-3 – ident: 21_CR50 doi: 10.21437/Interspeech.2021-599 – volume: 27 start-page: 040013 year: 2017 ident: 21_CR17 publication-title: Proc. Meet. Acoust. – volume: 155 start-page: 2549 year: 2024 ident: 21_CR134 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0025543 – volume: 9 year: 2019 ident: 21_CR125 publication-title: Sci. Rep. doi: 10.1038/s41598-019-48909-4 – volume: 31 start-page: 6572 year: 2018 ident: 21_CR72 publication-title: Adv. Neural Inf. Process. Syst. – ident: 21_CR111 – volume: 111 start-page: 21117 year: 2023 ident: 21_CR73 publication-title: Nonlinear Dyn. doi: 10.1007/s11071-023-08933-6 – ident: 21_CR65 – ident: 21_CR119 doi: 10.1145/2939672.2939778 – volume: 200 start-page: 110535 year: 2023 ident: 21_CR10 publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2023.110535 – ident: 21_CR12 doi: 10.1007/978-3-662-45620-0 – ident: 21_CR14 doi: 10.1109/ICASSP40776.2020.9053327 – ident: 21_CR13 doi: 10.21437/Interspeech.2019-2821 – ident: 21_CR66 doi: 10.1109/ICASSP39728.2021.9414750 – volume: 620 start-page: 47 year: 2023 ident: 21_CR146 publication-title: Nature doi: 10.1038/s41586-023-06221-2 – ident: 21_CR43 doi: 10.1109/ICASSP49357.2023.10096813 – volume: 48 start-page: 1127 year: 2023 ident: 21_CR71 publication-title: IEEE J. Ocean. Eng. doi: 10.1109/JOE.2023.3292417 – ident: 21_CR4 – ident: 21_CR67 doi: 10.1109/ICASSP49357.2023.10095801 – volume: 151 start-page: 104751 year: 2021 ident: 21_CR145 publication-title: Computers Geosci. doi: 10.1016/j.cageo.2021.104751 – volume: 156 start-page: 4229 year: 2024 ident: 21_CR107 publication-title: J. Acoustical Soc. Am. doi: 10.1121/10.0034707 – volume: 390 start-page: 114474 year: 2022 ident: 21_CR144 publication-title: Computer Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2021.114474 – ident: 21_CR46 doi: 10.1109/ICASSP40776.2020.9054580 – volume: 156 start-page: A78 year: 2024 ident: 21_CR148 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0035175 – volume: 37 start-page: 1727 year: 2021 ident: 21_CR6 publication-title: Acta Mechanica Sin. doi: 10.1007/s10409-021-01148-1 – volume: 146 start-page: 3590 year: 2019 ident: 21_CR5 publication-title: J. Acoust. Soc. Am. doi: 10.1121/1.5133944 – volume: 49 start-page: 62 year: 2023 ident: 21_CR94 publication-title: Adv. Computational Math. doi: 10.1007/s10444-023-10065-9 – volume: 13 start-page: 347 year: 2019 ident: 21_CR51 publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2019.2909077 – volume: 152 start-page: 107 year: 2022 ident: 21_CR9 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0011809 – volume: 9 start-page: 84956 year: 2021 ident: 21_CR29 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3087697 – ident: 21_CR45 doi: 10.1109/ICASSP40776.2020.9053905 – volume: 63 start-page: 411 year: 2001 ident: 21_CR141 publication-title: J. R. Stat. Soc. B doi: 10.1111/1467-9868.00293 – volume: 63 start-page: 139 year: 2020 ident: 21_CR37 publication-title: Commun. Acm. doi: 10.1145/3422622 – volume: 156 start-page: 812 year: 2024 ident: 21_CR97 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0028177 – ident: 21_CR34 doi: 10.21437/Interspeech.2018-1113 – volume: 155 start-page: 105233 year: 2023 ident: 21_CR140 publication-title: Computers Geotech. doi: 10.1016/j.compgeo.2022.105223 – ident: 21_CR59 doi: 10.21437/Interspeech.2024-1333 – volume: 155 start-page: 3690 year: 2024 ident: 21_CR87 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0026231 – volume: 3 start-page: 861 year: 2018 ident: 21_CR114 publication-title: J. Open Source Softw. doi: 10.21105/joss.00861 – volume: 39 start-page: 1 year: 2020 ident: 21_CR84 publication-title: J. Nondestructive Evaluation doi: 10.1007/s10921-020-00705-1 – ident: 21_CR90 – volume: 55 start-page: 1 year: 2023 ident: 21_CR112 publication-title: ACM Comput. Surv. doi: 10.1145/3561048 – ident: 21_CR55 doi: 10.1109/CVPR52688.2022.01042 – volume: 14 start-page: 1500 year: 2023 ident: 21_CR133 publication-title: Methods Ecol. Evol. doi: 10.1111/2041-210X.14103 – volume: 155 start-page: 1048 year: 2024 ident: 21_CR81 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0024750 – ident: 21_CR57 doi: 10.1109/ICASSP48485.2024.10446761 – ident: 21_CR63 – volume: 92 start-page: 1011 year: 2021 ident: 21_CR139 publication-title: Seismol. Res. Lett. doi: 10.1785/0220200164 – ident: 21_CR110 – volume: 6 start-page: 30 year: 2025 ident: 21_CR142 publication-title: IEEE Open J. Signal Process. doi: 10.1109/OJSP.2025.3528330 – ident: 21_CR104 – volume: 82 start-page: 1059 year: 2020 ident: 21_CR118 publication-title: J. R. Stat. Soc. Ser. B: Stat. Methodol. doi: 10.1111/rssb.12377 – ident: 21_CR47 doi: 10.21437/Interspeech.2021-1820 – ident: 21_CR68 doi: 10.1109/ICASSP48485.2024.10448477 – volume: 21 start-page: 7834 year: 2021 ident: 21_CR83 publication-title: Sensors doi: 10.3390/s21237834 – volume: 361 start-page: 418 year: 2024 ident: 21_CR19 publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2023.11.038 – volume: 1 start-page: 1 year: 2023 ident: 21_CR11 publication-title: Intell. Mar. Technol. Syst. doi: 10.1007/s44295-023-00005-0 – ident: 21_CR39 – ident: 21_CR52 – volume: 20 start-page: 1 year: 2019 ident: 21_CR117 publication-title: J. Mach. Learn. Res. – ident: 21_CR16 – ident: 21_CR41 – volume: 22 start-page: 3033 year: 2022 ident: 21_CR124 publication-title: Sensors doi: 10.3390/s22083033 – volume: 379 start-page: 20200093 year: 2021 ident: 21_CR76 publication-title: Philos. Trans. R. Soc. A doi: 10.1098/rsta.2020.0093 – volume: 157 start-page: 493 year: 2025 ident: 21_CR136 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0034856 – ident: 21_CR120 – ident: 21_CR69 doi: 10.1109/WASPAA58266.2023.10248178 – volume: 384 start-page: 113938 year: 2021 ident: 21_CR91 publication-title: Computer Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2021.113938 – ident: 21_CR109 doi: 10.1109/ICASSP48485.2024.10446097 – volume: 7 start-page: 122644 year: 2019 ident: 21_CR128 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2938227 – ident: 21_CR131 doi: 10.1109/OCEANS47191.2022.9977333 – ident: 21_CR64 – volume: 43 start-page: A3055 year: 2021 ident: 21_CR88 publication-title: SIAM J. Sci. Comput. doi: 10.1137/20M1318043 – volume: 155 start-page: 2037 year: 2024 ident: 21_CR78 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0025235 – volume: 421 start-page: 116813 year: 2024 ident: 21_CR143 publication-title: Computer Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2024.116813 – volume: 104 start-page: 808 year: 1998 ident: 21_CR102 publication-title: J. Acoust. Soc. Am. doi: 10.1121/1.423355 – volume: 67 start-page: 705 year: 2019 ident: 21_CR23 publication-title: J. Audio Eng. Soc. doi: 10.17743/jaes.2019.0024 – volume: 31 start-page: 433 year: 2020 ident: 21_CR127 publication-title: J. intell., manuf. doi: 10.1007/s10845-018-1456-1 – volume: 155 start-page: 3410 year: 2024 ident: 21_CR32 publication-title: J. Acoustical Soc. Am. doi: 10.1121/10.0026026 – ident: 21_CR80 doi: 10.1121/10.0009057 – volume: 153 start-page: 738 year: 2023 ident: 21_CR132 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0016887 – ident: 21_CR115 – volume: 147 start-page: 4087 year: 2020 ident: 21_CR25 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0001461 – volume: 378 start-page: 686 year: 2019 ident: 21_CR74 publication-title: J. Computational Phys. doi: 10.1016/j.jcp.2018.10.045 – volume: 3 start-page: e1602614 year: 2017 ident: 21_CR70 publication-title: Sci. Adv. doi: 10.1126/sciadv.1602614 – volume: 70 start-page: 9555 year: 2021 ident: 21_CR49 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2021.3102302 – ident: 21_CR36 – ident: 21_CR2 doi: 10.1016/B978-0-12-801522-3.00012-4 – ident: 21_CR28 doi: 10.1029/2021JB021716 – ident: 21_CR121 doi: 10.1609/aaai.v32i1.11491 – volume: 13 start-page: 863 year: 2019 ident: 21_CR20 publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2019.2917582 – volume: 128 start-page: 106872 year: 2023 ident: 21_CR85 publication-title: Ultrasonics doi: 10.1016/j.ultras.2022.106872 – volume: 11 start-page: e1424 year: 2021 ident: 21_CR113 publication-title: Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. – ident: 21_CR123 – ident: 21_CR30 doi: 10.1121/10.0036312 – volume: 71 start-page: 241 year: 2023 ident: 21_CR26 publication-title: J. Aud. Eng. Soc. doi: 10.17743/jaes.2022.0066 – volume: 16 start-page: 199 year: 2001 ident: 21_CR101 publication-title: Stat. Sci. doi: 10.1214/ss/1009213726 – ident: 21_CR21 doi: 10.1109/WASPAA52581.2021.9632672 – ident: 21_CR40 doi: 10.21437/Interspeech.2021-230 – volume: 358 start-page: 112623 year: 2020 ident: 21_CR77 publication-title: Computer Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2019.112623 – volume: 41 start-page: 60 year: 2025 ident: 21_CR82 publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2024.3465896 – volume: 22 start-page: 1 year: 2021 ident: 21_CR116 publication-title: J. Mach. Learn. Res. – volume: 148 start-page: 1101 year: 2020 ident: 21_CR106 publication-title: J. Acoust. Soc. Am. doi: 10.1121/10.0001731 – ident: 21_CR61 – volume: 36 start-page: 962 year: 2021 ident: 21_CR92 publication-title: Computer-Aided Civ. Infrastruct. Eng. doi: 10.1111/mice.12685 – ident: 21_CR60 doi: 10.1109/ICASSP49660.2025.10889311 – ident: 21_CR58 – volume: 12 start-page: 982 year: 2023 ident: 21_CR86 publication-title: Axioms doi: 10.3390/axioms12100982 – ident: 21_CR33 |
| SSID | ssib058497124 |
| Score | 2.3055782 |
| SecondaryResourceType | review_article |
| Snippet | Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review,... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 18 |
| SubjectTerms | 639/166/987 639/766/930/1032 639/766/930/12 Acoustics Algorithms Audio data Bioacoustics Classification Control Datasets Deep learning Dynamical Systems Earth sciences Engineering Acoustics High level languages Machine learning Materials Science Neural networks Noise Control Physics Physics and Astronomy Programming languages Python Review Ultrasound Vibration |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3fS8MwED7m9uKLKCpOp-TBNw1r0zTNBJFNNobgEHGwt9D8El-6nzL8703SlqGgz4XQftfcXe6-fAdwTXLj8gxisc4iiynLbfCDmFsXfphkkstAkJ2w8ZQ-zdJZAyb1XRhPq6x9YnDUeq58jbybeGk736OKHhZL7KdG-e5qPUIjr0Yr6PsgMbYHLeKVsZrQGgwnL6_1H-aibS9zEa26PRMlvLumNOEU-6muUeArbH9GqF3a-atTGgLQ6BAOqswR9UtTH0HDFMcweA5kSIMqndR39FGgvnvLIL98h_qoLP6jvNDIk0dwWa1HPvF2nzZffZ3AdDR8exzjaiwCVg7WLXYJS8a1jpU7zamedJ5OuX0ndc96OTGv8WYoN5FiVLI0iPHkcWYJ1YTHystvnUKzmBfmDJA1TJo0Ta3hhKqU5JHKM6ptxEzsTlKkDTc1FGJRql-I0LVOuCiBEw44EYAT2zZ0arREtRPWYme3NtzWCO4e_73a-f-rXcA-CUbzjK8ONDerT3Pp8oONvKqM_g3Mx7a4 priority: 102 providerName: ProQuest |
| Title | Machine Learning in Acoustics: A Review and Open-source Repository |
| URI | https://link.springer.com/article/10.1038/s44384-025-00021-w https://www.proquest.com/docview/3249208990 |
| Volume | 1 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: Springer Nature HAS Fully OA customDbUrl: eissn: 3005-141X dateEnd: 99991231 omitProxy: true ssIdentifier: ssib058497124 issn: 3005-141X databaseCode: AAJSJ dateStart: 0 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwGP3YDwQv4k-czpGDNxfssjTNvHVjcwwcog52C02ayC6d2Mn-fZO0ZUzx4KnQNj28fM33ku_lBeCWJNryDGJwGgUGU5YYPw5ibmz6YZJJLr1Ads6mCzpbhssadKu9MHv1e2_dnVPa5xS7Y1cDLyjY1qHJbWDyBjTjePY6q-LH5tJBZPNVuTfGNr__3Xg__-xI5Y86qE8vk2M4KnkhiouOPIGazk7hwOszVX4GwycvetSo9EN9R6sMxWrtz-LKH1CMikV-lGQpciIRXKzKI0ew85WrpJ_DYjJ-G01xefwBVha-LbbEJOJp2lN21qYG0o5oyv5fMh0YZxvmvNw05TpQjEoWetOdpBcZQlPCe8rZbF1AI1tn-hKQ0UzqMAyN5oSqkCSBSiKamoDpnp0xkRbcVaCIj8LlQvjqdJ-LAkJhIRQeQrFtQbvCTZQRn4u-sx50NcSgBd0Ky93jv7929b_Xr-GQ-O50Sq82NDafX_rG8oKN7ECdTx47ZVDY63A8f36xd0ds9A1TJbGf |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LS8NAEB5qPehFFBXrcw960sV0s0m2QpH6KK3VIqLgbU32IV5StUrxz_nbnN0kFAW9eQ4M4dvJfDOZ2W8AdllqMM9gluoksJTHqfVxkAqL9BNncSYyPyA7jHt3_OI-uq_BZ3UXxo1VVjHRB2o9Uu4f-WHopO1cjyo4fn6hbmuU665WKzTScrWCbnuJsfJix8B8TLCEG7f7Z3jee4x1z29Pe7TcMkAVvuWEIv8nQuumwuJItTIMHArdONMt69S5nGSa4cIEKuZZHHltm7SZWMY1E03l1KzQ7gzM8pC3sPibPTkfXt9UHo3s3kqQQcvbOkEoDsech4JTt0U28PMRk--MOE1zf3RmPeF1F2GhzFRJp3CtJaiZfBlOrvzwpSGlLusjecpJB1Hxcs9HpEOKZgNJc03csAotugPEJfoI5ej1YwXu_gWgVajno9ysAbEmzkwURdYIxlXE0kClCdc2iE0TKzfWgP0KCvlcqG1I3yUPhSyAkwic9MDJSQM2K7Rk-eWN5dRPGnBQITh9_Lu19b-t7cBc7_bqUl72h4MNmGf-AN202SbU317fzRbmJm_ZdukABB7-2-e-AH-t8PA |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LTsMwEFyVViAuiKcoFPCBGxhSx3FcbuFRlQIVElTiZiV-oF7SihT197GdRBUIDpwTWdFkvTv2jscApyTVlmcQg1UcGExZanwexNzY8sMylvHMC2RHbDCmw7forQGsPgvjRfve0tKn6VoddllQGnKK3eWrgZcVLC5myqxAi8chs9HcSpLhy7COJFtVe7GtXNUpmSDkvwzwvRIt6eWPjqgvNP1N2KgYIkrKb9qChs63YdUrNWWxA9dPXv6oUeWM-o4mOUrk1N_KVVyhBJXb_SjNFXJyEVzuzyNHtYuJ66nvwrh_93ozwNVFCFhaIBfYUpSYK9WVdv0me5nNbdLOtEz1jDMQc65umnIdSEYzFnn7nbQbG0IV4V3pDLf2oJlPc70PyGiW6SiKjOaEyoikgUxjqkzAdNeunUgbzmpQxKz0uxC-Tx1yUUIoLITCQygWbejUuIkq9gsROhNC100M2nBeY7l8_PdoB_97_QTWnm_74vF-9HAI68T_WSf_6kBz_vGpjyxZmGfHVWR8AU_TtrQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine+Learning+in+Acoustics%3A+A+Review+and+Open-source+Repository&rft.jtitle=npj+Acoustics&rft.au=McCarthy%2C+Ryan+A.&rft.au=Zhang%2C+You&rft.au=Verburg%2C+Samuel+A.&rft.au=Jenkins%2C+William+F.&rft.date=2025-09-09&rft.issn=3005-141X&rft.eissn=3005-141X&rft.volume=1&rft.issue=1&rft_id=info:doi/10.1038%2Fs44384-025-00021-w&rft.externalDBID=n%2Fa&rft.externalDocID=10_1038_s44384_025_00021_w |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=3005-141X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=3005-141X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=3005-141X&client=summon |