Abstract
Speech-music overlap detection in audio signals is an essential preprocessing step for many high-level audio processing applications. Speech and music spectrograms exhibit characteristic harmonic striations that can be used as a feature for detecting their overlap. Hence, this work proposes two features generated using a spectral peak tracking algorithm to capture prominent harmonic patterns in spectrograms. One feature consists of the spectral peak amplitude evolutions in an audio interval. The second feature is designed as a Mel-scaled spectrogram obtained by suppressing non-peak spectral components. In addition, a one-dimensional convolutional neural network architecture is proposed to learn the temporal evolution of spectral peaks. Mel-spectrogram is used as a baseline feature to compare performances. A popular public dataset MUSAN with 102 h of data has been used to perform experiments. A late fusion of the proposed features with baseline is observed to provide better performance.
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Supported by Visvesvaraya PhD Scheme, MeitY, Govt. of India - MEITY-PHD-1230.
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Bhattacharjee, M., Prasanna, S.R.M., Guha, P. (2022). Speech Music Overlap Detection Using Spectral Peak Evolutions. In: Prasanna, S.R.M., Karpov, A., Samudravijaya, K., Agrawal, S.S. (eds) Speech and Computer. SPECOM 2022. Lecture Notes in Computer Science(), vol 13721. Springer, Cham. https://doi.org/10.1007/978-3-031-20980-2_8
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