Schulze-Forster et al., 2023 - Google Patents
Unsupervised music source separation using differentiable parametric source modelsSchulze-Forster et al., 2023
View PDF- Document ID
- 8137284158404813176
- Author
- Schulze-Forster K
- Richard G
- Kelley L
- Doire C
- Badeau R
- Publication year
- Publication venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
External Links
Snippet
Supervised deep learning approaches to underdetermined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corresponding isolated source signals. Such datasets can be extremely costly to obtain for musical …
Classifications
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- G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/003—Changing voice quality, e.g. pitch or formants
- G10L21/007—Changing voice quality, e.g. pitch or formants characterised by the process used
- G10L21/013—Adapting to target pitch
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