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 (Lead / Corresponding author), Huimin Zhao, Sophia Zhao, John Soraghan, Tariq Durrani

Computing

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

Abstract

In this paper, we present an efficient approach to detect and tracking the fundamental frequency (F0) from ‘wav’ audio. In general, music F0 and harmonic frequency show the multiple relations; therefore frequency domain analysis can be used to track the F0. 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 F0 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, Min Jia, Jiasong Mu, Wei Wang, Xuhong Feng, Baoju Zhang
Place of Publication	Singapore
Publisher	Springer Singapore
Pages	591-599
Number of pages	9
Edition	1
ISBN (Electronic)	9789811065712
ISBN (Print)	9789811065705, 9789811338878
DOIs	https://doi.org/10.1007/978-981-10-6571-2_72
Publication status	Published - 2019
Event	6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 - Harbin, China Duration: 14 Jul 2017 → 16 Jul 2017

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	463
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

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

Keywords

Automatic music transcription
Fundamental frequency detection
Multiple pitch estimation
Polyphonic music segmentation

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

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

Cite this

Li, X., Yan, Y., Ren, J., Zhao, H., Zhao, S., Soraghan, J., & Durrani, T. (2019). Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis. In Q. Liang, M. Jia, J. Mu, W. Wang, X. Feng, & B. Zhang (Eds.), Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems (1 ed., pp. 591-599). (Lecture Notes in Electrical Engineering; Vol. 463). Springer Singapore. 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 ; Min Jia ; Jiasong Mu ; Wei Wang ; Xuhong Feng ; Baoju Zhang. 1. ed. Singapore : Springer Singapore, 2019. pp. 591-599 (Lecture Notes in Electrical Engineering).

@inproceedings{7de8350444264e83b76ba6e1176e2ac4,

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 (F0) from {\textquoteleft}wav{\textquoteright} audio. In general, music F0 and harmonic frequency show the multiple relations; therefore frequency domain analysis can be used to track the F0. 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 F0 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, Fundamental frequency detection, Multiple pitch estimation, Polyphonic music segmentation",

author = "Xiaoquan Li and Yijun Yan and Jinchang Ren and Huimin Zhao and Sophia Zhao and John Soraghan and Tariq Durrani",

note = "Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 ; Conference date: 14-07-2017 Through 16-07-2017",

year = "2019",

doi = "10.1007/978-981-10-6571-2_72",

language = "English",

isbn = "9789811065705",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Singapore",

pages = "591--599",

editor = "Qilian Liang and Min Jia and Jiasong Mu and Wei Wang and Xuhong Feng and Baoju Zhang",

booktitle = "Communications, Signal Processing, and Systems",

address = "Singapore",

edition = "1",

}

Li, X, Yan, Y, Ren, J, Zhao, H, Zhao, S, Soraghan, J & Durrani, T 2019, Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis. in Q Liang, M Jia, J Mu, W Wang, X Feng & B Zhang (eds), Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. 1 edn, Lecture Notes in Electrical Engineering, vol. 463, Springer Singapore, Singapore, pp. 591-599, 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017, 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 (Lead / Corresponding author) et al.
Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. ed. / Qilian Liang; Min Jia; Jiasong Mu; Wei Wang; Xuhong Feng; Baoju Zhang. 1. ed. Singapore: Springer Singapore, 2019. p. 591-599 (Lecture Notes in Electrical Engineering; Vol. 463).

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

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

N1 - Copyright: © Springer Nature Singapore Pte Ltd. 2019.

PY - 2019

Y1 - 2019

N2 - In this paper, we present an efficient approach to detect and tracking the fundamental frequency (F0) from ‘wav’ audio. In general, music F0 and harmonic frequency show the multiple relations; therefore frequency domain analysis can be used to track the F0. 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 F0 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 (F0) from ‘wav’ audio. In general, music F0 and harmonic frequency show the multiple relations; therefore frequency domain analysis can be used to track the F0. 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 F0 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 - Fundamental frequency detection

KW - Multiple pitch estimation

KW - Polyphonic music segmentation

UR - http://www.scopus.com/inward/record.url?scp=85048680567&partnerID=8YFLogxK

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

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

M3 - Conference contribution

AN - SCOPUS:85048680567

SN - 9789811065705

SN - 9789811338878

T3 - Lecture Notes in Electrical Engineering

SP - 591

EP - 599

BT - Communications, Signal Processing, and Systems

A2 - Liang, Qilian

A2 - Jia, Min

A2 - Mu, Jiasong

A2 - Wang, Wei

A2 - Feng, Xuhong

A2 - Zhang, Baoju

PB - Springer Singapore

CY - Singapore

T2 - 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017

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, Jia M, Mu J, Wang W, Feng X, Zhang B, editors, Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. 1 ed. Singapore: Springer Singapore. 2019. p. 591-599. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-10-6571-2_72

Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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