Simona Domazetovska, Viktor Gavriloski, Maja Anachkova, Zlatko Petreski

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The application of the advanced methods for noise analysis in the urban areas through the development of systems for classification of sound events significantly improves and simplifies the process of noise assessment. The main purpose of sound recognition and classification systems is to develop algorithms that can detect and classify sound events that occur in the chosen environment, giving an appropriate response to their users. In this research, a supervised system for recognition and classification of sound events has been established through the development of feature extraction techniques based on digital signal processing of the audio signals that are further used as an input parameter in the machine learning algorithms for classification of the sound events. Various audio parameters were extracted and processed in order to choose the best set of parameters that result in better recognition of the class to which the sounds belong. The created acoustic event detection and classification (AED/C) system could be further implemented in sound sensors for automatic control of environmental noise using the source classification that leads to reduced amount of required human validation of the sound level measurements since the target noise source is evidently defined.


Sound recognition, audio parametrization, machine learning, urban noise

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Socoró, J. C., Sevillano, X., & Alías, F. Analysis and automatic detection of anomalous noise events in real recordings of road traffic noise for the LIFE DYNAMAP project. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings (Vol. 253, No. 6, pp. 1943-1952),. Institute of Noise Control Engineering. (August, 2016). Available: https://doi.org/10.1515/noise-2018-0006

Socoró, J. C., Alías, F., Alsina R. M, Sevillano X., Q. B3-Report describing the ANED algorithms for low and high computation capacity sensors. 2016

Bello, J. P., Silva, C., Nov, O., Dubois, R. L., Arora, A., Salamon, J. Sonyc: A system for monitoring, analyzing, and mitigating urban noise pollution. Communications of the ACM, 62(2), 68-77. 2019. Available: https://doi.org/10.1145/3224204

Hollosi, D., Nagy, G., Rodigast, R., Goetze, S., & Cousin, P.. Enhancing wireless sensor networks with acoustic sensing technology: use cases, applications & experiments. In 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE CPS Computing (pp. 335-342). IEEE. (2013, August) Available: 10.1109/GreenCom-iThings-CPSCom.2013.75

Su, Y., Zhang, K., Wang, J., Zhou, D., & Madani, K. Performance analysis of multiple aggregated acoustic features for environment sound classification. Applied Acoustics, 158, 107050. (2020) Available: https://doi.org/10.1016/j.apacoust.2019.107050

Alsouda,Y., Pllana,S., Kurti, A.. Iot-based urban noise identification using machine learning: performance of SVM, KNN, bagging, and random forest. In Proceedings of the international conference on omni-layer intelligent systems (pp. 62-67). (2019, May) Available: https://doi.org/10.1145/3312614.3312631

Zhang, Zhichao, et al. "Learning attentive representations for environmental sound classification." IEEE Access 7 (2019): 130327-130339.Fu Z., Lu, G., Ming Ting, K., Zhang, D. A survey of audio-based music classification and annotation [Journal] // IEEE Transactions on Multimedia. 2:Vol.13. - pp. 303-319. (April 2011) DOI: 10.1109/TMM.2010.2098858

Mushtaq, Z., Su, S. F., & Tran, Q. V. Spectral images based environmental sound classification using CNN with meaningful data augmentation. Applied Acoustics, 172, 107581. (2021) Available: https://doi.org/10.1016/j.apacoust.2020.107581

Geiger, J., & Helwani, K. Improving event detection for audio surveillance using Gabor filterbank features. In 23rd European Signal Processing Conference (pp. 714–718). (2015) Available: 10.1109/EUSIPCO.2015.7362476

Mulimani, M., & Koolagudi, S. G. Segmentation and characterization of acoustic event spectrograms using singular value decomposition. Expert Systems with Applications, 120 413–425. (2019) Available: https://doi.org/10.1016/j.eswa.2018.12.004

J. Schroder, B. Cauchi, M. R. Schadler, N. Moritz, K. Adiloglu, J. Anemuller, S. Doclo, B. Kollmeier, and S. Goetze, “Acoustic event detection using signal enhancement and spectro-temporal feature extraction,” in IEEE Workshop on Applicat. Signal Process. Audio Acoust.(WASPAA), pp. 1–3. 2013

Liu, Chengwei, et al. "Environmental Sound Classification Based on Stacked Concatenated DNN using Aggregated Features." Journal of Signal Processing Systems 1-13 (2021). Available: https://doi.org/10.1007/s11265-021-01702-x

Abdoli, S., Cardinal, P., & Koerich, A. L. End-to-end environmental sound classification using a 1D convolutional neural network. Expert Systems with Applications, 136, 252-263. (2019) Available: https://doi.org/10.1016/j.asoc.2019.105912

Salamon, J., Jacoby, C., & Bello, J. P. A dataset and taxonomy for urban sound research. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 1041-1044). (November, 2014) Available: https://doi.org/10.1145/2647868.2655045

Temko, A., Nadeu, C., Macho, D., Malkin, R., Zieger, C., & Omologo, M.. Acoustic event detection and classification. In Computers in the human interaction loop (pp. 61-73). (2009). Springer, London

Mitrović, D., Zeppelzauer, M., & Breiteneder, C. Features for content-based audio retrieval. In Advances in computers (Vol. 78, pp. 71-150). Elsevier. (2010) Available: https://doi.org/10.1016/S0065-2458(10)78003-7

K. Wang and C. Xu. Robust soccer highlight generation with a novel dominant-speech feature extractor. In Proceedings of the IEEE International Conference on Multimedia and Expo, volume 1, pages 591–594, Taipei, Taiwan, Jun. 2004. IEEE, IEEE. 10.1109/ICME.2004.1394261

Chang, C., & Doran, B. Urban Sound Classification: With Random Forest SVM DNN RNN and CNN Classifiers. In CSCI E-81 Machine Learning and Data Mining Final Project Fall 2016. Harvard University Cambridge. 2016

DOI: https://doi.org/10.22190/FUACR211015012D


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