The article discusses the development of a new model for speech enhancement called SICRN. The model uses a state space model and inplace convolution techniques to enhance speech quality, especially…
Continue readingThe article presents a new domain adaptation method for speech enhancement, termed as Remixed2Remixed. It uses Noise2Noise learning to adapt models trained on artificially generated noisy-clean pair data to enhance…
Continue readingThe researchers propose a novel model called Dual-Phase Audio Transformer for Denoising (DPATD) to address the challenges of time-domain speech enhancement systems. DPATD splits the audio input into smaller chunks,…
Continue readingThe article presents a new deep complex hybrid transformer for speech enhancement, which combines approaches from both the spectrogram and waveform domains. The model, comprised of a complex Swin-Unet in…
Continue readingThis article presents research into the development of a universal speech enhancement model that can handle diverse input conditions. Existing techniques have shown strong performance but are typically designed for…
Continue readingThe article introduces a Unified Speech Enhancement and Editing (uSee) model that uses conditional diffusion models to perform various tasks simultaneously. The model can control the generation of unified speech…
Continue readingThe ICASSP 2023 Acoustic Echo Cancellation Challenge intends to stimulate research in acoustic echo cancellation, a crucial area of speech enhancement. The challenge has been enhanced by adding a track…
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