ConcateNet: Dialogue Separation Using Local And Global Feature Concatenation
<|reference_start|>ConcateNet: Dialogue Separation Using Local And Global Feature Concatenation: Dialogue separation involves isolating a dialogue signal from a mixture, such as a movie or a TV program. This can be a necessary step to enable dialogue enhancement for broadcast-related applications. In this paper, ConcateNet for dialogue separation is proposed, which is based on a novel approach for processing local and global features aimed at better generalization for out-of-domain signals. ConcateNet is trained using a noise reduction-focused, publicly available dataset and evaluated using three datasets: two noise reduction-focused datasets (in-domain), which show competitive performance for ConcateNet, and a broadcast-focused dataset (out-of-domain), which verifies the better generalization performance for the proposed architecture compared to considered state-of-the-art noise-reduction methods.<|reference_end|>
@article{halimeh2024concatenet:,
    title={ConcateNet: Dialogue Separation Using Local And Global Feature
  Concatenation},
    author={Mhd Modar Halimeh, Matteo Torcoli, Emanu"el Habets},
    journal={arXiv preprint arXiv:2408.08729},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08729},
    primaryClass={eess.AS cs.CL cs.SD}
}