short-paper

Effective Music Feature NCP: Enhancing Cover Song Recognition with Music Transcription

Authors:

Xiaoshuo XuAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 925 - 928

https://doi.org/10.1145/3077136.3080680

Published: 07 August 2017 Publication History

Abstract

Chroma is a widespread feature for cover song recognition, as it is robust against non-tonal components and independent of timbre and specific instruments. However, Chroma is derived from spectrogram, thus it provides a coarse approximation representation of musical score. In this paper, we proposed a similar but more effective feature Note Class Profile (NCP) derived with music transcription techniques. NCP is a multi-dimensional time serie, each column of which denotes the energy distribution of 12 note classes. Experimental results on benchmark datasets demonstrated its superior performance over existing music features. In addition, NCP feature can be enhanced further with the development of music transcription techniques. The source code can be found in github¹.

References

[1]

Juan Pablo Bello. 2007. Audio-Based Cover Song Retrieval Using Approximate Chord Sequences: Testing Shifts, Gaps, Swaps and Beats. In International Society for Music Information Retrieval Conference.

[2]

Thierry Bertin-Mahieux and Daniel P. W. Ellis. 2012. Large-Scale Cover Song Recognition Using the 2D Fourier Transform Magnitude. International Society for Music Information Retrieval Conference.

[3]

Ning Chen, J. Stephen Downie, Haidong Xiao, Yu Zhu, and Jie Zhu. 2015. Modified Perceptual Linear Prediction Liftered Cepstrum (MPLPLC) Model for Pop Cover Song Recognition. In International Society for Music Information Retrieval Conference.

[4]

Daniel P. W. Ellis and Graham E. Poliner. 2007. Identifying Cover Songs with Chroma Features and Dynamic Programming Beat Tracking. IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]

Emilia Gómez. 2006. Tonal Description of Polyphonic Audio for Music Content Processing INFORMS Journal on Computing.

[6]

Eric J. Humphrey, Oriol Nieto, and Juan Pablo Bello. 2013. Data Driven and Discriminative Projections for Large-Scale Cover Song Identification. International Society for Music Information Retrieval Conference.

[7]

Nanzhu Jiang, Peter Grosche, Verena Konz, and Meinard Müller. 2011. Analyzing Chroma Feature Types for Automated Chord Recognition. Audio Engineering Society Conference: 42nd International Conference: Semantic Audio. Audio Engineering Society.

[8]

Maksim Khadkevich and Maurizio Omologo. 2013. Large-Scale Cover Song Identification Using Chord Profiles. International Society for Music Information Retrieval Conference.

[9]

Henrique B. S. Leão, Germano F Guimarães, Geber L. Ramalho, Sérgio V. Cavalcante, and others. 2003. Benchmarking Wave-to-MIDI Transcription Tools. In University of São Paulo.

[10]

Kyogu Lee. 2006. Identifying Cover Songs from Audio Using Harmonic Representation. MIREX task on Audio Cover Song Identification.

[11]

Meinard Müller, Frank Kurth, and Michael Clausen. 2005. Audio Matching via Chroma-Based Statistical Features. International Society for Music Information Retrieval Conference.

[12]

Julien Osmalsky, Jean-Jacques Embrechts, Peter Foster, and Simon Dixon. 2015. Combining Features for Cover Song Identification. International Society for Music Information Retrieval Conference.

[13]

Joan Serra. 2011. Identification of Versions of the Same Musical Composition by Processing Audio Descriptions. Department of Information and Communication Technologies.

[14]

Diego Furtado Silva, Vinícius Mourão Alves de Souza, Gustavo Enrique de Almeida Prado Alves Batista, and others. 2015. Music Shapelets for Fast Cover Song Regognition. International Society for Music Information Retrieval Conference.

[15]

J. M. H. van Balen, Dimitrios Bountouridis, Frans Wiering, Remco C. Veltkamp, and others. 2014. Cognition-inspired Descriptors for Scalable Cover Song Retrieval. International Society for Music Information Retrieval Conference.

Cited By

Zhao JTaniar DAdhinugraha KBaskaran VWong K(2023)Multi-mmlg: a novel framework of extracting multiple main melodies from MIDI filesNeural Computing and Applications10.1007/s00521-023-08924-z35:30(22687-22704)Online publication date: 16-Aug-2023
https://doi.org/10.1007/s00521-023-08924-z
Jiang CYang DChen X(2020)Learn A Robust Representation For Cover Song Identification Via Aggregating Local And Global Music Temporal Context2020 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME46284.2020.9102975(1-6)Online publication date: Jul-2020
https://doi.org/10.1109/ICME46284.2020.9102975
Yu ZXu XChen XYang D(2020)Learning a Representation for Cover Song Identification Using Convolutional Neural NetworkICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP40776.2020.9053839(541-545)Online publication date: May-2020
https://doi.org/10.1109/ICASSP40776.2020.9053839
Show More Cited By

Index Terms

Effective Music Feature NCP: Enhancing Cover Song Recognition with Music Transcription
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures
    2. Retrieval tasks and goals
      1. Sentiment analysis

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Published In

cover image ACM Conferences

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2017

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Natural Science Foundation of China

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SIGIR '17

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SIGIR

SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Zhao JTaniar DAdhinugraha KBaskaran VWong K(2023)Multi-mmlg: a novel framework of extracting multiple main melodies from MIDI filesNeural Computing and Applications10.1007/s00521-023-08924-z35:30(22687-22704)Online publication date: 16-Aug-2023
https://doi.org/10.1007/s00521-023-08924-z
Jiang CYang DChen X(2020)Learn A Robust Representation For Cover Song Identification Via Aggregating Local And Global Music Temporal Context2020 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME46284.2020.9102975(1-6)Online publication date: Jul-2020
https://doi.org/10.1109/ICME46284.2020.9102975
Yu ZXu XChen XYang D(2020)Learning a Representation for Cover Song Identification Using Convolutional Neural NetworkICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP40776.2020.9053839(541-545)Online publication date: May-2020
https://doi.org/10.1109/ICASSP40776.2020.9053839
Jiang CYang DChen X(2020)Similarity Learning For Cover Song Identification Using Cross-Similarity Matrices of Multi-Level Deep SequencesICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP40776.2020.9053257(26-30)Online publication date: May-2020
https://doi.org/10.1109/ICASSP40776.2020.9053257
Yu ZXu XChen XYang D(2019)Temporal pyramid pooling convolutional neural network for cover song identificationProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367717(4846-4852)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367717
Xu XChen XYang D(2018)Key-Invariant Convolutional Neural Network Toward Efficient Cover Song Identification2018 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME.2018.8486531(1-6)Online publication date: Jul-2018
https://doi.org/10.1109/ICME.2018.8486531

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