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- W4312069047 abstract "Music Information Retrieval is a crucial task which has ample opportunities in Music Industries. Currently, audio engineers have to create custom karaoke tracks manually for songs. The technique of producing a high-quality karaoke track for a song is not accessible to the public. Audacity and other specialised software must be needed to generate karaoke. In this work, we review different methods and approaches, which give a high-quality karaoke track by presenting a simple and quick separation of vocals from a given song with both vocal and instrumental components. It does not need the use of any specific audio processing software. We review techniques and approaches for generating karaoke such as Spleeter, Hybrid Demucs, D3Net, Open-Unmix, Sams-Net etc. These approaches are based on current state-of-the-art machine learning and deep learning techniques. We believe that this review will serve the purpose as a good resource for researchers working in this field." @default.
- W4312069047 created "2023-01-04" @default.
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- W4312069047 date "2022-11-07" @default.
- W4312069047 modified "2023-10-01" @default.
- W4312069047 title "Karaoke Generation from songs: recent trends and opportunities" @default.
- W4312069047 doi "https://doi.org/10.23919/apsipaasc55919.2022.9980133" @default.
- W4312069047 hasPublicationYear "2022" @default.
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