Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis

Xiaoquan Li; Yijun Yan; Jinchang Ren; Huimin Zhao; Sophia Zhao; John Soraghan; Tariq Durrani

doi:10.1007/978-981-10-6571-2_72

Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis

Xiaoquan Li, Yijun Yan, Jinchang Ren, Huimin Zhao, Sophia Zhao, John Soraghan, Tariq Durrani

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

29 Downloads (Pure)

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 language	English
Title of host publication	Communications, Signal Processing, and Systems
Subtitle of host publication	Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems
Editors	Qilian Liang, Jiasong Mu, Min Jia, Wei Wang, Xuhong Feng, Baoju Zhang
Place of Publication	Singapore
Publisher	Springer
Pages	591-599
Number of pages	9
ISBN (Print)	9789811065705
DOIs	https://doi.org/10.1007/978-981-10-6571-2_72
Publication status	E-pub ahead of print - 7 Jun 2018
Event	6th International Conference on Communications, Signal Processing, and Systems - Harbin, China Duration: 14 Jul 2017 → 16 Jul 2017

Conference

Conference	6th International Conference on Communications, Signal Processing, and Systems
Abbreviated title	CSPS 2017
Country/Territory	China
City	Harbin
Period	14/07/17 → 16/07/17

Keywords

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

Access to Document

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

Li-etal-ICCSPS-2017-Knowledge-based-fundamental-and-harmonic-frequency-detectionAccepted author manuscript, 532 KB

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. Advance online publication. https://doi.org/10.1007/978-981-10-6571-2_72

Li, Xiaoquan ; Yan, Yijun ; Ren, Jinchang et al. / 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 = jun,

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",

note = "6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 ; Conference date: 14-07-2017 Through 16-07-2017",

}

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 et al.
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 proceeding › Conference 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

T2 - 6th International Conference on Communications, Signal Processing, and Systems

Y2 - 14 July 2017 through 16 July 2017

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 Epub 2018 Jun 7. doi: 10.1007/978-981-10-6571-2_72

Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this