research-article

Non-reference audio quality assessment for online live music recordings

Authors:

Ju-Chiang Wang,

Ye WangAuthors Info & Claims

MM '13: Proceedings of the 21st ACM international conference on Multimedia

Pages 63 - 72

https://doi.org/10.1145/2502081.2502106

Published: 21 October 2013 Publication History

Abstract

Immensely popular video sharing websites such as YouTube have become the most important sources of music information for Internet users and the most prominent platform for sharing live music. The audio quality of this huge amount of live music recordings, however, varies significantly due to factors such as environmental noise, location, and recording device. However, most video search engines do not take audio quality into consideration when retrieving and ranking results. Given the fact that most users prefer live music videos with better audio quality, we propose the first automatic, non-reference audio quality assessment framework for live music video search online. We first construct two annotated datasets of live music recordings. The first dataset contains 500 human-annotated pieces, and the second contains 2,400 synthetic pieces systematically generated by adding noise effects to clean recordings. Then, we formulate the assessment task as a ranking problem and try to solve it using a learning-based scheme. To validate the effectiveness of our framework, we perform both objective and subjective evaluations. Results show that our framework significantly improves the ranking performance of live music recording retrieval and can prove useful for various real-world music applications.

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Cited By

Cao YMin XSun WZhai G(2023)Subjective and Objective Audio-Visual Quality Assessment for User Generated ContentIEEE Transactions on Image Processing10.1109/TIP.2023.329052832(3847-3861)Online publication date: 2023
https://doi.org/10.1109/TIP.2023.3290528
Cao YMin XSun WZhang XZhai G(2023)Audio-Visual Quality Assessment for User Generated Content: Database and Method2023 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP49359.2023.10222476(1495-1499)Online publication date: 8-Oct-2023
https://doi.org/10.1109/ICIP49359.2023.10222476
Mumtaz DJakhetiya VNathwani KSubudhi BGuntuku S(2022)Nonintrusive Perceptual Audio Quality Assessment for User-Generated Content Using Deep LearningIEEE Transactions on Industrial Informatics10.1109/TII.2021.313901018:11(7780-7789)Online publication date: Dec-2022
https://doi.org/10.1109/TII.2021.3139010
Show More Cited By

Index Terms

Non-reference audio quality assessment for online live music recordings
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

MM '13: Proceedings of the 21st ACM international conference on Multimedia

October 2013

1166 pages

ISBN:9781450324045

DOI:10.1145/2502081

General Chairs:
Alejandro (Alex) Jaimes
Yahoo!, Spain
,
Nicu Sebe
University of Trento, Italy
,
Nozha Boujemaa
INRIA, France
,
Program Chairs:
Daniel Gatica-Perez
IDIAP & EPFL, Switzerland
,
David A. Shamma
Yahoo!, USA
,
Marcel Worring
University of Amsterdam, The Netherlands
,
Roger Zimmermann
National University of Singapore, Singapore

Copyright © 2013 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2013

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MM '13: ACM Multimedia Conference

October 21 - 25, 2013

Barcelona, Spain

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MM '13 Paper Acceptance Rate 47 of 235 submissions, 20%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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https://doi.org/10.1109/TIP.2023.3290528
Cao YMin XSun WZhang XZhai G(2023)Audio-Visual Quality Assessment for User Generated Content: Database and Method2023 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP49359.2023.10222476(1495-1499)Online publication date: 8-Oct-2023
https://doi.org/10.1109/ICIP49359.2023.10222476
Mumtaz DJakhetiya VNathwani KSubudhi BGuntuku S(2022)Nonintrusive Perceptual Audio Quality Assessment for User-Generated Content Using Deep LearningIEEE Transactions on Industrial Informatics10.1109/TII.2021.313901018:11(7780-7789)Online publication date: Dec-2022
https://doi.org/10.1109/TII.2021.3139010
Falkowski-Gilski P(2021)Digital Transformation of Terrestrial Radio: An Analysis of Simulcasted Broadcasts in FM and DAB+ for a Smart and Successful SwitchoverApplied Sciences10.3390/app11231111411:23(11114)Online publication date: 23-Nov-2021
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Becerra Martinez HHines AFarias M(2021)Perceptual Quality of Audio-Visual Content with Common Video and Audio DegradationsApplied Sciences10.3390/app1113581311:13(5813)Online publication date: 23-Jun-2021
https://doi.org/10.3390/app11135813
Xie JZhu YZhang ZPeng JYi JHu YLiu HChen Z(2020)A Multimodal Variational Encoder-Decoder Framework for Micro-video Popularity PredictionProceedings of The Web Conference 202010.1145/3366423.3380004(2542-2548)Online publication date: 20-Apr-2020
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Simou NMastorakis YStefanakis N(2020)Towards Blind Quality Assessment of Concert Audio Recordings Using Deep Neural NetworksICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP40776.2020.9053356(3477-3481)Online publication date: May-2020
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Nie LLiu MSong X(2019)Multimodal Learning toward Micro-Video UnderstandingSynthesis Lectures on Image, Video, and Multimedia Processing10.2200/S00938ED1V01Y201907IVM0209:4(1-186)Online publication date: 17-Sep-2019
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