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Automatic Lyrics Transcription using Dilated Convolutional Neural Networks with Self-Attention
Centre for Digital Music Queen Mary University of London.
Royal College of Music in Stockholm, Department of Folk Music. Kungliga Musikhögskolan, Stockholm.ORCID iD: 0000-0002-4756-1441
Centre for Digital Music Queen Mary University of London.
2020 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Speech recognition is a well developed research field so that the current state of the art systems are being used in many applications in the software industry, yet as by today, there still does not exist such robust system for the recognition of words and sentences from singing voice. This paper proposes a complete pipeline for this task which may commonly be referred as automatic lyrics transcription (ALT). We have trained convolutional time-delay neural networks with self-attention on monophonic karaoke recordings using a sequence classification objective for building the acoustic model. The dataset used in this study, DAMP - Sing! 300x30x2 [1] is filtered to have songs with only English lyrics. Different language models are tested including MaxEnt and Recurrent Neural Networks based methods which are trained on the lyrics of pop songs in English. An in-depth analysis of the self-attention mechanism is held while tuning its context width and the number of attention heads. Using the best settings, our system achieves significant improvement to the state-of-the-art in ALT and provides a new baseline for the task.

Place, publisher, year, edition, pages
2020.
Keywords [en]
automatic speech recognition, machine learning, deep learning, music information retrieval, automatic lyrics transcription, language modeling
National Category
Signal Processing Musicology
Identifiers
URN: urn:nbn:se:kmh:diva-3739OAI: oai:DiVA.org:kmh-3739DiVA, id: diva2:1502264
Conference
International Joint Conference on Neural Networks (IJCNN). IEEE, 2020
Note

Available from: 2020-11-19 Created: 2020-11-19 Last updated: 2020-12-15Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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Output format
  • html
  • text
  • asciidoc
  • rtf