Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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.
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

Fingerprint

Frequency domain analysis
Processing

Keywords

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

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). Singapore: Springer. https://doi.org/10.1007/978-981-10-6571-2_72
Li, Xiaoquan ; Yan, Yijun ; Ren, Jinchang ; Zhao, Huimin ; Zhao, Sophia ; Soraghan, John ; Durrani, Tariq. / Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis. Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. editor / Qilian Liang ; Jiasong Mu ; Min Jia ; Wei Wang ; Xuhong Feng ; Baoju Zhang. Singapore : Springer, 2018. pp. 591-599
@inproceedings{969f48fe5a3b4374801ad00ba7cf23e5,
title = "Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis",
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.",
keywords = "automatic music transcription, multiple pitch estimation, polyphonic music segmentation, fundamental frequency detection",
author = "Xiaoquan Li and Yijun Yan and Jinchang Ren and Huimin Zhao and Sophia Zhao and John Soraghan and Tariq Durrani",
year = "2018",
month = "6",
day = "7",
doi = "10.1007/978-981-10-6571-2_72",
language = "English",
isbn = "9789811065705",
pages = "591--599",
editor = "Qilian Liang and Jiasong Mu and Min Jia and Wei Wang and Xuhong Feng and Baoju Zhang",
booktitle = "Communications, Signal Processing, and Systems",
publisher = "Springer",

}

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. Springer, Singapore, pp. 591-599, 6th International Conference on Communications, Signal Processing, and Systems, Harbin, China, 14/07/17. https://doi.org/10.1007/978-981-10-6571-2_72

Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis. / Li, Xiaoquan; Yan, Yijun; Ren, Jinchang; Zhao, Huimin; Zhao, Sophia; Soraghan, John; Durrani, Tariq.

Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. ed. / Qilian Liang; Jiasong Mu; Min Jia; Wei Wang; Xuhong Feng; Baoju Zhang. Singapore : Springer, 2018. p. 591-599.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis

AU - Li, Xiaoquan

AU - Yan, Yijun

AU - Ren, Jinchang

AU - Zhao, Huimin

AU - Zhao, Sophia

AU - Soraghan, John

AU - Durrani, Tariq

PY - 2018/6/7

Y1 - 2018/6/7

N2 - 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.

AB - 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.

KW - automatic music transcription

KW - multiple pitch estimation

KW - polyphonic music segmentation

KW - fundamental frequency detection

UR - https://link.springer.com/book/10.1007/978-981-10-6571-2

U2 - 10.1007/978-981-10-6571-2_72

DO - 10.1007/978-981-10-6571-2_72

M3 - Conference contribution book

SN - 9789811065705

SP - 591

EP - 599

BT - Communications, Signal Processing, and Systems

A2 - Liang, Qilian

A2 - Mu, Jiasong

A2 - Jia, Min

A2 - Wang, Wei

A2 - Feng, Xuhong

A2 - Zhang, Baoju

PB - Springer

CY - Singapore

ER -

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