Abstract
Regular pitch detection algorithms are known to be immensely useful for speech source analysis. Their utility is not as reliable when processing polyphonic acoustic mixtures like Music. This is an investigative study of music components like rhythm, accompaniment and Lyrical-voicing, that is seen as a critical task towards targeted music component identification and processing. Popular music forms like Western and Hindustani Classical are considered for our study dataset. For Western cases, comparative preliminary analysis of the spectral characteristics like Harmonics and Energy is done towards characterization of Music region against that of Lyrics-music mixture. \(F_{0}\) contour analysis for these regions, using Autocorrelation and Zero frequency filtering indicates the utility of the latter in Lyrical-voicing onset identification. Short-time spectral analysis leads to the distinctive understanding about the Harmonic structure according to the music polyphony. Strength of Excitation is found to be insightful towards characterizing sounds like base sounds, prominent in percussion instruments. For study on Classical music, \(F_{0}\) contour analysis using raw signal and LP Residual elucidate the characteristic average pitch effect, which comes out to be higher for the Alaap region in case of Female artists and Lyrics composition regions for the Male artists, giving cues towards the applications like Raaga identification and summarization. The analysis of the excitation source features for various music components done in this work present some insightful observations and clues towards effective Music component processing.
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Notes
- 1.
Ocean of Ragas: A dedicated collection of 1800+ Ragas of Hindustani Classical Music [Online]. http://www.oceanofragas.com.
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Sharma, S., Ghisingh, S., Mittal, V.K. (2018). Component Characterization of Western and Indian Classical Music. In: Thampi, S., Krishnan, S., Corchado Rodriguez, J., Das, S., Wozniak, M., Al-Jumeily, D. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2017. Advances in Intelligent Systems and Computing, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-67934-1_6
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