Abstract
Differentiation of speech and song from acoustic signal is a challenging issue. It is a significant part of automatic classification of audio. Most of the previous works have been done for classifying speech and non-speech, but comparatively less work has been done for differentiating speech and song. Mostly, frequency and perceptual domain features were common in those works. In this work, a small dimensional acoustic feature has been proposed. Speech differs from song due to the absence of instrumental part within it which is present in song and causes increase of energy for song signal compared to speech signal. Short-time energy (STE), an acoustic feature, can reflect this observation. For precise study of energy variation, features based on very small change of energy, Delta Energy, and co-occurrence matrix of it are considered. For classification purpose, some well-known classifiers have been employed. Experimental result has been compared with existing methodologies to reflect the efficiency of the proposed system.
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Ghosal, A., Yasmin, G., Banerjee, D. (2019). Differentiation of Speech and Song Using Occurrence Pattern of Delta Energy. In: Nayak, J., Abraham, A., Krishna, B., Chandra Sekhar, G., Das, A. (eds) Soft Computing in Data Analytics . Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-13-0514-6_74
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DOI: https://doi.org/10.1007/978-981-13-0514-6_74
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