Abstract
For effective multimedia content, retrieval audio data plays an important role. Recognising classes of audio data which is neither music nor speech is a challenging task; in this aspect, the authors proposed to work on environment sounds. To represent the audio data, low-level features are extracted. These low-level descriptors are computed from both time domain and frequency domain representation of audio data. From the extracted descriptors, midterm statistics are computed and an information system (IS) is built with class labels. From this IS using the concept of rough set theory, reducts are computed, and from the reducts, rules are generated. The rules obtained are tested against the test set sampled from ESC-10 dataset.
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Prathima, T., Govardhan, A., Ramadevi, Y. (2020). Rough Set-Based Classification of Audio Data. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_53
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DOI: https://doi.org/10.1007/978-981-15-1480-7_53
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