Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis

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Abstract

In this paper, we present an efficient approach to detect and tracking the fundamental frequency (Fo) from 'wav' audio. In general, music Fo and harmonic frequency show the multiple relations; therefore frequency domain analysis can be used to track the Fo. The model includes the harmonic frequency probability analysis method and useful pre-post processing for multiple instruments. Thus, the proposed system can efficiently transcribe polyphonic music, while taking into account the probability of Fo and harmonic frequency. The experimental results demonstrate that the proposed system can successful transcribe polyphonic music, achieved the quite advanced level.
Original languageEnglish
Title of host publicationCommunications, Signal Processing, and Systems
Subtitle of host publicationProceedings of the 2017 International Conference on Communications, Signal Processing, and Systems
EditorsQilian Liang, Jiasong Mu, Min Jia, Wei Wang, Xuhong Feng, Baoju Zhang
Place of PublicationSingapore
PublisherSpringer
Pages591-599
Number of pages9
ISBN (Print)9789811065705
DOIs
Publication statusE-pub ahead of print - 7 Jun 2018
Event6th International Conference on Communications, Signal Processing, and Systems - Harbin, China
Duration: 14 Jul 201716 Jul 2017

Conference

Conference6th International Conference on Communications, Signal Processing, and Systems
Abbreviated titleCSPS 2017
CountryChina
CityHarbin
Period14/07/1716/07/17

Keywords

  • automatic music transcription
  • multiple pitch estimation
  • polyphonic music segmentation
  • fundamental frequency detection

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  • Cite this

    Li, X., Yan, Y., Ren, J., Zhao, H., Zhao, S., Soraghan, J., & Durrani, T. (2018). Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis. In Q. Liang, J. Mu, M. Jia, W. Wang, X. Feng, & B. Zhang (Eds.), Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems (pp. 591-599). Springer. https://doi.org/10.1007/978-981-10-6571-2_72