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Multimodal Human-Robot Interaction from the Perspective of a Speech Scientist Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Automatic Sound Recognition of Urban Environment Events

Authors : Theodoros Theodorou, Iosif Mporas, Nikos Fakotakis

Published in: Speech and Computer

Publisher: Springer International Publishing

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Abstract

The audio analysis of speaker’s surroundings has been a first step for several processing systems that enable speaker’s mobility though his daily life. These algorithms usually operate in a short-time analysis decomposing the incoming events in time and frequency domain. In this paper, an automatic sound recognizer is studied, which investigates audio events of interest from urban environment. Our experiments were conducted using a close set of audio events from which well known and commonly used audio descriptors were extracted and models were training using powerful machine learning algorithms. The best urban sound recognition performance was achieved by SVMs with accuracy equal to approximately 93 %.

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Literature
1.
2.
Aucouturier, J.J., Defreville, B., Pachet, F.: The bag-of-frames approach to audio pattern recognition: a sufficient model for urban soundscapes but not for polyphonic music. J. Acoust. Soc. Am. 122(2), 881–891 (2007)CrossRef
3.
Bartsch, M.A., Wakefield, G.H.: Audio thumbnailing of popular music using chroma-based representations. IEEE Trans. Multimedia 7(1), 96–104 (2005)CrossRef
4.
Casey, M.: General sound classification and similarity in MPEG-7. Organised Sound 6(02), 153–164 (2001)CrossRef
5.
Couvreur, L., Laniray, M.: Automatic noise recognition in urban environments based on artificial neural networks and hidden markov models. InterNoise, Prague, Czech Republic, pp. 1–8 (2004)
6.
Dogan, E., Sert, M., Yazici, A.: Content-based classification and segmentation of mixed-type audio by using mpeg-7 features. In:First International Conference on Advances in Multimedia, MMEDIA 2009, pp. 152–157. IEEE (2009)
7.
Eyben, F., Wöllmer, M., Schuller, B.: Opensmile: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the international conference on Multimedia, pp. 1459–1462. ACM (2010)
8.
Fernandez, L.P.S., Ruiz, A.R., de JM Juarez, J.: Urban noise permanent monitoring and pattern recognition. In: Proceedings of the European Conference of Communications-ECCOM, vol. 10, pp. 143–148 (2010)
9.
Huang, R., Hansen, J.H.: Advances in unsupervised audio classification and segmentation for the broadcast news and NGSW corpora. IEEE Trans. Audio Speech Lang. Process. 14(3), 907–919 (2006)CrossRef
10.
Khunarsal, P., Lursinsap, C., Raicharoen, T.: Very short time environmental sound classification based on spectrogram pattern matching. Inf. Sci. 243, 57–74 (2013)CrossRef
11.
Kim, H.G., Moreau, N., Sikora, T.: Audio classification based on MPEG-7 spectral basis representations. IEEE Trans. Circuits Syst. Video Technol. 14(5), 716–725 (2004)CrossRef
12.
Kinnunen, T., Saeidi, R., Leppänen, J., Saarinen, J.P.: Audio context recognition in variable mobile environments from short segments using speaker and language recognizers. In: The Speaker and Language Recognition Workshop, pp. 301–311 (2012)
13.
Lee, K., Slaney, M.: Automatic chord recognition from audio using a HMM with supervised learning. In: ISMIR, pp. 133–137 (2006)
14.
Lu, H., Pan, W., Lane, N.D., Choudhury, T., Campbell, A.T.: Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of the 7th international conference on Mobile systems, applications, and services, pp. 165–178. ACM (2009)
15.
Ntalampiras, S.: Universal background modeling for acoustic surveillance of urban traffic. Digital Signal Process. 31, 69–78 (2014)CrossRef
16.
Ntalampiras, S., Potamitis, I., Fakotakis, N.: Exploiting temporal feature integration for generalized sound recognition. EURASIP J. Adv. Sig. Process. 2009(1), 807162 (2009)CrossRef
17.
Patsis, Y., Verhelst, W.: A speech/music/silence/garbage/classifier for searching and indexing broadcast news material. In: 19th International Workshop on Database and Expert Systems Application, DEXA 2008, pp. 585–589. IEEE (2008)
18.
Salamon, J., Jacoby, C., Bello, J.P.: A dataset and taxonomy for urban sound research. In: Proceedings of the ACM International Conference on Multimedia, pp. 1041–1044. ACM (2014)
19.
Slaney, M.: Auditory toolbox. Interval Research Corporation. Technical report vol. 10 (1998)
20.
Smith, J.W., Pijanowski, B.C.: Human and policy dimensions of soundscape ecology. Global Environ. Change 28, 63–74 (2014)CrossRef
21.
Torija, A., Diego, P.R., Ramos-Ridao, A.: Ann-based m events. a too against envi environment (2011)
22.
Tran, H.D., Li, H.: Sound event recognition with probabilistic distance SVMs. IEEE Trans. Audio Speech Lang. Process. 19(6), 1556–1568 (2011)CrossRef
23.
Valero, X., Alías, F., Oldoni, D., Botteldooren, D.: Support vector machines and self-organizing maps for the recognition of sound events in urban soundscapes. In: 41st International Congress and Exposition on Noise Control Engineering (Inter-Noise-2012). Institute of Noise Control Engineering (2012)
24.
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2005)
Metadata
Title
Automatic Sound Recognition of Urban Environment Events
Authors
Theodoros Theodorou
Iosif Mporas
Nikos Fakotakis
Copyright Year
2015
Book
Speech and Computer

Print ISBN: 978-3-319-23131-0

Electronic ISBN: 978-3-319-23132-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-23132-7_16

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