Audio-Based Detection of Explicit Content in Music

Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence D'Alche-Buc

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a novel automatic system for performing explicit content detection directly on the audio signal. Our modular approach uses an audio-to-character recognition model, a keyword spotting model associated with a dictionary of carefully chosen keywords, and a Random Forest classification model for the final decision. To the best of our knowledge, this is the first explicit content detection system based on audio only. We demonstrate the individual relevance of our modules on a set of sub-tasks and compare our approach to a lyrics-informed oracle and an end-to-end naive architecture. The results obtained are encouraging with a F1-score of 67% on a industrial scale explicit content dataset.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages526-530
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - 1 May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • CTC training
  • Explicit content detection
  • keyword spotting
  • lyrics transcription
  • music information retrieval

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