research-article

Machine Learning Algorithms for Environmental Sound Recognition: Towards Soundscape Semantics

Authors:

Vasileios Bountourakis,

Lazaros Vrysis,

George PapanikolaouAuthors Info & Claims

AM '15: Proceedings of the Audio Mostly 2015 on Interaction With Sound

Article No.: 5, Pages 1 - 7

https://doi.org/10.1145/2814895.2814905

Published: 07 October 2015 Publication History

Abstract

This paper investigates methods aiming at the automatic recognition and classification of discrete environmental sounds, for the purpose of subsequently applying these methods to the recognition of soundscapes. Research in audio recognition has traditionally focused on the domains of speech and music. Comparatively little research has been done towards recognizing non-speech environmental sounds. For this reason, in this paper, we apply existing techniques that have been proved efficient in the other two domains. These techniques are comprehensively compared to determine the most appropriate one for addressing the problem of environmental sound recognition.

References

[1]

Bountourakis, V. Semantic Analysis of Environmental Sounds through Audio Feature Extraction and use of Machine Learning Methods, Aristotle University of Thessaloniki, 2015.

[2]

Bullock, J., and Conservatoire, U. C. E. B. Libxtract: A lightweight library for audio feature extraction. In Proceedings of the International Computer Music Conference (Vol. 43), 2007.

[3]

Cannam, C., Landone, C., Sandler, M. B., and Bello, J. P. The Sonic Visualiser: A Visualisation Platform for Semantic Descriptors from Musical Signals. In ISMIR, 2006, 324--327.

[4]

Chachada, S., and Kuo, C. C. J. Environmental sound recognition: A survey. APSIPA Transactions on Signal and Information Processing, 2014, 3, e14.

[5]

Chu, S., Narayanan, S., and Kuo, C. J. Environmental sound recognition with time--frequency audio features. Audio, Speech, and Language Processing, IEEE Transactions on, 2009, 17(6), 1142--1158.

Digital Library

[6]

Cowling, M., and Sitte, R. Comparison of techniques for environmental sound recognition. Pattern recognition letters, 2003, 24(15), 2895--2907.

Digital Library

[7]

Cowling, M. Non-Speech Environmental Sound Classification System for Autonomous Surveillance (Doctoral dissertation, Faculty of Engineering and Information Technology, Griffith University, Gold Coast), 2004.

[8]

Dimoulas C., Vegiris C., Avdelidis K., Kalliris G. and Papanikolaou G. Automated Audio Detection, Segmentation, and Indexing with Application to Postproduction Editing, in Proceedings of the 122nd AES Convention, paper no. 7138, 2007.

[9]

Dimoulas C., and Kalliris G. Investigation of wavelet approaches for joint temporal, spectral and cepstral features in audio semantics, in Proceedings of the 134th AES Convention, pp. 509--518, 2013.

[10]

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 2009 11(1), 10--18.

Digital Library

[11]

Kotsakis R., Kalliris G., and Dimoulas C. Investigation of broadcast-audio semantic analysis scenarios employing radio-programme-adaptive pattern classification, Speech Communication, vol. 54, no. 6, pp. 743--762, 2012.

Digital Library

[12]

Kotsakis R., Kalliris G., and Dimoulas C. Investigation of salient audio-features for pattern-based semantic content analysis of radio productions, in Proceedings of the 132nd AES Convention, pp. 513--520, 2012.

[13]

Mitrović, D., Zeppelzauer, M., & Eidenberger, H. On feature selection in environmental sound recognition. In ELMAR, 2009. ELMAR'09. International Symposium (pp. 201--204). IEEE.

[14]

Mitrović, D., Zeppelzauer, M., & Breiteneder, C. Features for content-based audio retrieval. Advances in computers, 2010, 78, 71--150.

[15]

Ntalampiras, S., Potamitis, and I., Fakotakis, N. Automatic Recognition of urban environmental sound events, 2008.

[16]

Peeters, G. A large set of audio features for sound description (similarity and classification) in the CUIDADO project, 2004.

[17]

Powers, D. M. Evaluation: from precision, recall and -Fmeasure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2 (1), 37--63, 2011.

[18]

Salamon, J., and Gómez, E. Mir.edu: An open-source library for teaching sound and music description. in Proceedings of the 15th International Society for Music Information Retrieval (ISMIR), Tapei, Taiwan, 2014.

[19]

Tsau, E., Chachada, S., and Kuo, C. C. J. Content/context-adaptive feature selection for environmental sound recognition. In Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012, Asia-Pacific (pp. 1--5). IEEE.

[20]

Tsipas, N., Dimoulas, C. A., Kalliris, G. M., and Papanikolaou, G. Collaborative annotation platform for audio semantics. in Audio Engineering Society Convention 134. Audio Engineering Society, 2013.

[21]

Uzkent, B., Barkana, B. D., and Cevikalp, H. Non-speech environmental sound classification using SVMs with a new set of features. International Journal of Innovative Computing, Information and Control, 2012, 8(5B), 3511--3524.

[22]

Vegiris C., Dimoulas C. and Papanikolaou G. Audio Content Annotation, Description and Management Using Joint Audio Detection, Segmentation and Classification Techniques, in Proceedings of the 126th AES Convention, paper no. 7661, Munich, May 7-- 10, 2009.

[23]

Vrysis, L., Dimoulas, C. A., Kalliris, G. M., and Papanikolaou, G. Mobile Audio Measurements Platform: Towards Audio Semantic Intelligence into Ubiquitous Computing Environments. in Audio Engineering Society Convention 134. Audio Engineering Society, 2013.

Cited By

Rochadiani TArifin YSuhartono DBudiharto W(2024)Exploring Transfer Learning Approach for Environmental Sound Classification: A Comparative Analysis2024 International Conference on Smart Computing, IoT and Machine Learning (SIML)10.1109/SIML61815.2024.10578248(112-116)Online publication date: 6-Jun-2024
https://doi.org/10.1109/SIML61815.2024.10578248
Gourisaria MAgrawal RSahni MSingh P(2024)Comparative analysis of audio classification with MFCC and STFT features using machine learning techniquesDiscover Internet of Things10.1007/s43926-023-00049-y4:1Online publication date: 3-Jan-2024
https://doi.org/10.1007/s43926-023-00049-y
Albaji AAlbaji A(2024)Literature ReviewMachine Learning for Environmental Noise Classification in Smart Cities10.1007/978-3-031-54667-9_2(7-44)Online publication date: 22-Mar-2024
https://doi.org/10.1007/978-3-031-54667-9_2
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Machine Learning Algorithms for Environmental Sound Recognition: Towards Soundscape Semantics

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Published In

cover image ACM Other conferences

AM '15: Proceedings of the Audio Mostly 2015 on Interaction With Sound

October 2015

250 pages

ISBN:9781450338967

DOI:10.1145/2814895

Conference Chair:
George Kalliris
Aristotle University of Thessaloniki
,
Program Chair:
Charalampos Dimoulas
Aristotle University of Thessaloniki

Copyright © 2015 ACM.

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Published: 07 October 2015

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AM '15

AM '15: Audio Mostly 2015

October 7 - 9, 2015

Thessaloniki, Greece

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Cited By

Rochadiani TArifin YSuhartono DBudiharto W(2024)Exploring Transfer Learning Approach for Environmental Sound Classification: A Comparative Analysis2024 International Conference on Smart Computing, IoT and Machine Learning (SIML)10.1109/SIML61815.2024.10578248(112-116)Online publication date: 6-Jun-2024
https://doi.org/10.1109/SIML61815.2024.10578248
Gourisaria MAgrawal RSahni MSingh P(2024)Comparative analysis of audio classification with MFCC and STFT features using machine learning techniquesDiscover Internet of Things10.1007/s43926-023-00049-y4:1Online publication date: 3-Jan-2024
https://doi.org/10.1007/s43926-023-00049-y
Albaji AAlbaji A(2024)Literature ReviewMachine Learning for Environmental Noise Classification in Smart Cities10.1007/978-3-031-54667-9_2(7-44)Online publication date: 22-Mar-2024
https://doi.org/10.1007/978-3-031-54667-9_2
Lamrini MChkouri MTouhafi A(2023)Evaluating the Performance of Pre-Trained Convolutional Neural Network for Audio Classification on Embedded Systems for Anomaly Detection in Smart CitiesSensors10.3390/s2313622723:13(6227)Online publication date: 7-Jul-2023
https://doi.org/10.3390/s23136227
Sweet DZangrando EMeneghello F(2023)HIJACK: Learning-based Strategies for Sound Classification Robustness to Adversarial Noise2023 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP58114.2023.00082(338-343)Online publication date: Jun-2023
https://doi.org/10.1109/SMARTCOMP58114.2023.00082
Nguyen HJavaheri NFranke J(2023)Manipulation of Deformable Linear Objects Enabled by Sound-event Classification in the Manufacturing Environment2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)10.1109/IEEM58616.2023.10406462(1027-1031)Online publication date: 18-Dec-2023
https://doi.org/10.1109/IEEM58616.2023.10406462
Pfitzinger TWöhrle H(2023)Embedded Real-Time Human Activity Recognition on an ESP32-S3 Microcontroller Using Ambient Audio Data2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)10.1109/IDAACS58523.2023.10348926(459-464)Online publication date: 7-Sep-2023
https://doi.org/10.1109/IDAACS58523.2023.10348926
Zaheer RAhmad IHabibi DIslam KPhung Q(2023)A Survey on Artificial Intelligence-Based Acoustic Source IdentificationIEEE Access10.1109/ACCESS.2023.328398211(60078-60108)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3283982
Gorgeva ERobertson JVoss SHoogewerff J(2023)The potential of bioacoustics for surveying carrion insectsAustralian Journal of Forensic Sciences10.1080/00450618.2023.2295447(1-20)Online publication date: 18-Dec-2023
https://doi.org/10.1080/00450618.2023.2295447
Mutanu LGohil JGupta KWagio PKotonya G(2022)A Review of Automated Bioacoustics and General Acoustics Classification ResearchSensors10.3390/s2221836122:21(8361)Online publication date: 31-Oct-2022
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