An acoustic scene classification approach involving domestic violence using machine learning
Abstract
Classifying and detecting acoustic scenes is a rapidly developing area of research, as the signal produced by the sound of audio contains information that visual data cannot represent. In this paper we deal with the problem of detecting acoustic scenes involving domestic violence. To this end, we propose the use of a machine learning method using the SVM classifier to detect scenes of domestic violence of a man against a woman using sound. We present analysis of experiments with three different features extracted from the audios. As a result, we obtained the best performance using the MFCC feature achieving an accuracy of 73.14%.
Keywords:
acoustic scenes classification, machine learning, domestic violence
References
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Clavel, C., Vasilescu, I.,Devillers, L., Richard, G., Ehrette, T. (2008) “Fear-type emotion recognition for future audio-based surveillance systems”. Speech Communication. Volume 50, Issue 6, June 2008, Pages 487-503.
Guo, F., Shan, S., Wang, X. (2010) “Using One-Class SVMs and MP for Audio Recognition of Action Scenes”, Second International Workshop on Education Technology and Computer Science, 2010,401-404.
Schuller, B., Batliner, A., Steidl, S., Seppi, D. (2011) “Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge”. Speech Communication, vol. 53, no. 9/10, pp. 1062–1087, 2011.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É. (2011) “Scikit-learn: Machine Learning in Python”, JMLR 12, pp. 2825-2830, 2011.
Hwang, K., Lee, S. (2012) “Environmental Audio Scene and Activity Recognition through Mobile-based Crowdsourcing”, In: IEEE Transactions on Consumer Electronics, 2012,58(2):700-705.
Kishore, K. V. K., Satish, P. K. (2013) “Emotion recognition in speech using MFCC and wavelet features”, In: 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, 2013, pp. 842-847.
Su, F. (2014) “Auditory scene analysis and recognition with LDA topic model”, In: IEEE International Conference on Multimedia and Expo,2014,1-6.
Ooi, C. S., Seng, K. P., Ang, L. M., Chew, L. W. (2014) “A new approach of audio emotion recognition”. Expert Systems with Applications 41 (13) (2014) 5858–5869.
Fernández-Delgado, M., Cernadas, E., Barro, S., and Amorim, D. (2014) “Do we need hundreds of classifiers to solve real world classification problems?”, The Journal of Machine Learning Research, 15(1), 3133-3181.
Iriya, R. (2014) “Análise de sinais de voz para reconhecimento de emoções”, Dissertação (Mestrado) — Curso de Engenharia e Sistemas Eletrônicos, Universidade de São Paulo, 2014.
Giannakopoulos, T. (2015) “pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis”, PLoS ONE 10(12): e0144610. doi:10.1371/ journal.pone.0144610.
Stowell, D., Giannoulis, D., Benetos, E., Lagrange, M., Plumbley, M. D. (2015) “Detection and Classification of Acoustic Scenes and Events”, In: IEEE Transactions on Multimedia, vol. 17, no. 10, pp. 1733-1746, Oct. 2015.
Goodfellow, I., Bengio, Y., Courville, A. (2016) “Deep Learning”. MIT Press http://www.deeplearningbook.org.
Yang, J., Cai, M., Li M., Jin, H. (2016) “Movie audio scene recognition based on WFST”, In: International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, 2016, pp. 77-80.
Elizalde, B., Kumar, A., Shah, A., Badlani, R., Vincent, E., Raj, B., Lane, I. (2016) “Experiments on the DCASE Challenge 2016: Acoustic scene classification and sound event detection in real life recording”, In: Proc. Workshop Detection Classification Acoust. Scenes Events, Budapest, Hungary, Sep. 2016, pp. 20-24.
Mu, G., Cao, H., Jin, Q. (2016) “Violent Scene Detection Using Convolutional Neural Networks and Deep Audio Features”. In: Tan T., Li X., Chen X., Zhou J., Yang J., Cheng H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore.
Mesaros, A., Heittola, T., Benetos, E., Foster, P., Lagrange, M., Virtanen, T., Plumbley, M. D. (2018) “Detection and classification of acoustic scenes and events: Outcome of the dcase 2016 challenge”, In: IEEE/ACM Transactions on Audio Speech and Language Processing vol. 26 no. 2 pp. 379-393 Feb 2018.
Gharib, S., Derrar, H., Niizumi, D., Senttula, T., Tommola, J., Heittola, T., Virtanen, T., Huttunen, H. (2018) “Acoustic scene classification: A competition review”. In: IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), pages 1–6. IEEE, 2018.
OO, M. M. (2018) “Comparative Study of MFCC Feature with Different Machine Learning Techniques in Acoustic Scene Classification”, In: International Journal of Research and Engineering ISSN: 2348-7860 (O) | 2348-7852 (P) | Vol. 5 No. 7 | July 2018 | PP. 439-444.
Sarman, S. e Sert, M. (2018) “Audio based violent scene classification using ensemble learning”, In: 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, 2018, pp. 1-5.
Dorogyy, Y., Kolisnichenko, V., Levchenko, K. (2018) “Violent Crime Detection System”, In: IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, 2018, pp. 352-355.
Andrade, R. O. (2019) “Faces da violência doméstica”, Revista Pesquisa FAPESP. Edição 277, Março, 2019. Disponível em:
Oliveira, C. A. B., Alencar, L. N., Cardena, R. R., Moreira, K. F. A., Pereira, P. P. S., Fernandes, D. E. R. (2019) “Perfil da vítima e características da violência contra a mulher no estado de Rondônia”, Brasil. Rev Cuid. 2019; 10(1): e573.
Published
2019-10-15
How to Cite
SOUTO, Helton; MELLO, Rafael; FURTADO, Ana.
An acoustic scene classification approach involving domestic violence using machine learning. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 705-716.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9327.
