Abstract
Discriminating song and speech from audio is an exigent problem. This is a step toward self-executing categorization in case of audio signal. Foregoing attempts were mostly involved for discriminating speech with nonspeech but relatively not much works were involved for classifying speech and song from audio signal. Mainly perceptual and frequency depended features were associated with the foregoing attempts. Song, whether it is associated with instrument or not, reveals some type of periodicity, whereas this periodicity is absent in case of normal speech. For accurate study of these periodic nature textural features based on its co-occurrence matrix along with its mean and standard deviation are also considered. Support Vector Machine (SVM), Neural Network (NN), and k-Nearest Neighbor (k-NN) have been brought into play for the purpose of taxonomy of speech from song. Speech and song classification precision obtained in this work has been compared with that of some other previous works done to reveal effectiveness of the advised feature set.
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Ghosal, A., Dutta, S., Banerjee, D. (2020). Classification of Speech and Song Using Co-occurrence-Based Approach. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_11
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DOI: https://doi.org/10.1007/978-981-13-9042-5_11
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