Abstract
Large amounts of audio data are available with the advent of technology. The role of audio data is decisive in analysing the data, be it activity recognition, event detection, etc. Classification of audio stream will help us to corroborate the results obtained from other media. We trained a CNN model to classify benchmark data sets ESC-10 and ESC-50. Along with these benchmark data sets, we tried a custom data set as well. CNN is trained on extracted low-level audio features from the custom and benchmark audio snippets which are both from constrained and noisy environments. We are able to identify CNN architecture with minimum layers which works good with both benchmark and custom data set. We also experimented to detect the most influencing feature which alone is sufficient to classify the multiple classes of audio data. Classification accuracy as high as 98% is reported.
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Prathima, T., Govardhan, A., Palla, S., Sri Yagna, K. (2022). Constrained and Unconstrained Audio Classifıcation. In: Pandian, A.P., Palanisamy, R., Narayanan, M., Senjyu, T. (eds) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-7330-6_75
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DOI: https://doi.org/10.1007/978-981-16-7330-6_75
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