short-paper

An Unsupervised Approach to Anomaly Detection in Music Datasets

Authors:

Yen-Cheng Lu,

Chih-Wei Wu,

Chang-Tien Lu,

Alexander LerchAuthors Info & Claims

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 749 - 752

https://doi.org/10.1145/2911451.2914700

Published: 07 July 2016 Publication History

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Abstract

This paper presents an unsupervised method for systematically identifying anomalies in music datasets. The model integrates categorical regression and robust estimation techniques to infer anomalous scores in music clips. When applied to a music genre recognition dataset, the new method is able to detect corrupted, distorted, or mislabeled audio samples based on commonly used features in music information retrieval. The evaluation results show that the algorithm outperforms other anomaly detection methods and is capable of finding problematic samples identified by human experts. The proposed method introduces a preliminary framework for anomaly detection in music data that can serve as a useful tool to improve data integrity in the future.

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Cited By

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Patel VHiran DDangarwala K(2024)Recent Trends of Information Retrieval System: Review Based on IR Models and ApplicationsProceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications10.1007/978-981-99-9442-7_51(619-629)Online publication date: 23-May-2024
https://doi.org/10.1007/978-981-99-9442-7_51
Su YYao DZhou XZhang YFan YBai LBi JChen HDuh WHuang HKato MMothe JPoblete B(2023)TripSafe: Retrieving Safety-related Abnormal Trips in Real-time with Trajectory DataProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592074(2446-2450)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592074
Mnasri ZRovetta SMasulli F(2022)Anomalous sound event detection: A survey of machine learning based methods and applicationsMultimedia Tools and Applications10.1007/s11042-021-11817-981:4(5537-5586)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-021-11817-9
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Index Terms

An Unsupervised Approach to Anomaly Detection in Music Datasets
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Anomaly detection
2. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Music retrieval

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Information

Published In

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2016

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SIGIR '16

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SIGIR

SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

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

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Cited By

View all

Patel VHiran DDangarwala K(2024)Recent Trends of Information Retrieval System: Review Based on IR Models and ApplicationsProceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications10.1007/978-981-99-9442-7_51(619-629)Online publication date: 23-May-2024
https://doi.org/10.1007/978-981-99-9442-7_51
Su YYao DZhou XZhang YFan YBai LBi JChen HDuh WHuang HKato MMothe JPoblete B(2023)TripSafe: Retrieving Safety-related Abnormal Trips in Real-time with Trajectory DataProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592074(2446-2450)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592074
Mnasri ZRovetta SMasulli F(2022)Anomalous sound event detection: A survey of machine learning based methods and applicationsMultimedia Tools and Applications10.1007/s11042-021-11817-981:4(5537-5586)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-021-11817-9
Aljably RAl-Rodhaan MTian Y(2022)Detecting Hybrid Anomalies Using an Unsupervised Approach in Online Social NetworksBig Data and Security10.1007/978-981-19-0852-1_50(630-642)Online publication date: 10-Mar-2022
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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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Bajaj ASharma TSangwan O(2020)Information Retrieval in Conjunction With Deep LearningHandbook of Research on Emerging Trends and Applications of Machine Learning10.4018/978-1-5225-9643-1.ch014(300-311)Online publication date: 2020
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Sattart FMcQuay CDriessen P(2017)Marine mammal sound anomaly and quality detection using multitaper spectrogram and hydrophone big data2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)10.1109/PACRIM.2017.8121916(1-6)Online publication date: Aug-2017
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Hayat MDaud A(2017)Anomaly detection in heterogeneous bibliographic information networks using co-evolution pattern miningScientometrics10.1007/s11192-017-2467-y113:1(149-175)Online publication date: 1-Oct-2017
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