Cold Diffusion for Speech Enhancement

Author:

Yen Hao1,Germain François G.1,Wichern Gordon1,Roux Jonathan Le1

Affiliation:

1. Mitsubishi Electric Research Laboratories (MERL),Cambridge,MA,USA

Publisher

IEEE

Reference41 articles.

1. Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation

2. Noisy speech database for training speech enhancement algorithms and TTS models;valentini-botinhao,2017

3. Speech Denoising with Deep Feature Losses

4. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

5. MetricGAN: Generative adversarial networks based black-box metric scores optimization for speech enhancement;fu;Proc ICML,2019

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2. Continuous lipreading based on acoustic temporal alignments;EURASIP Journal on Audio, Speech, and Music Processing;2024-05-06

3. Ensemble Inference for Diffusion Model-Based Speech Enhancement;2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW);2024-04-14

4. Scalable and Efficient Speech Enhancement Using Modified Cold Diffusion: A Residual Learning Approach;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

5. Single and Few-Step Diffusion for Generative Speech Enhancement;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

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