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MSTRE-Net: Multistr...
MSTRE-Net: Multistreaming Acoustic Modeling for Automatic Lyrics Transcription
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- Ahlbäck, Sven, 1960- (författare)
- Kungl. Musikhögskolan,Institutionen för folkmusik,Kungliga Musikhögskolan, Stockholm
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Kungl Musikhögskolan Institutionen för folkmusik (creator_code:org_t)
- 2021
- 2021
- Engelska.
- Relaterad länk:
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https://urn.kb.se/re...
Abstract
Ämnesord
Stäng
- This paper makes several contributions to automatic lyrics transcription (ALT) research. Our main contribution is a novel variant of the Multistreaming Time-Delay Neural Network (MTDNN) architecture, called MSTRE-Net, which processes the temporal information using multiple streams in parallel with varying resolutions keeping the network more compact, and thus with a faster inference and an improved recognition rate than having identical TDNN streams. In addition, two novel preprocessing steps prior to training the acoustic model are proposed. First, we suggest using recordings from both monophonic and polyphonic domains during training the acoustic model. Second, we tag monophonic and polyphonic recordings with distinct labels for discriminating non-vocal silence and music instances during alignment. Moreover, we present a new test set with a considerably larger size and a higher musical variability compared to the existing datasets used in ALT literature, while maintaining the gender balance of the singers. Our best performing model sets the state-of-the-art in lyrics transcription by a large margin. For reproducibility, we publicly share the identifiers to retrieve the data used in this paper.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
Nyckelord
- automatic lyrics transcription
- music information retrieval
- computational linguistics
- automatic speech recognition
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)