Don’t Separate, Learn To Remix: End-To-End Neural Remixing With Joint Optimization

Author:

Yang Haici1,Firodiya Shivani1,Bryan Nicholas J.2,Kim Minje1

Affiliation:

1. Indiana University,Luddy School of Informatics, Computing, and Engineering,Bloomington,IN,USA

2. Adobe Research,San Francisco,CA,USA

Publisher

IEEE

Reference29 articles.

1. Class-conditional Embeddings for Music Source Separation

2. Audio query-based music source separation;lee;Proc of the International Society for Music Information Retrieval Conference (ISMIR),2019

3. Bootstrapping deep music separation from primitive auditory grouping principles;seetharaman;Workshop on Self-supervision in Audio and Speech at International Conference on Machine Learning,2019

4. Deep clustering: Discriminative embeddings for segmentation and separation

5. Wave-U-Net: A multi-scale neural network for end-to-end audio source separation;stoller;Proc of the International Society for Music Information Retrieval Conference (ISMIR),2018

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1. Improving Music Source Separation with Simo Stereo Band-Split Rnn;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. General Purpose Audio Effect Removal;2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA);2023-10-22

3. Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2023

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