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cdiffuse

Conditional Diffusion Probabilistic Model for Speech Enhancement

clarity

Clarity Challenge toolkit - software for building Clarity Challenge systems

clearbuds

Clearbuds machine learning repository

clearbuds-fw

cmgan

Conformer-based Metric GAN for speech enhancement

cocktail-fork-separation

Baseline multi-resolution cross network model trained using the Divide and Remaster Dataset

cocosda-ssl

pytorch code for sound event localization and classification

complex-mtassnet

Multi-Task Audio Source Separation, Two-Stage Model, Complex Domain.

complex_neural_source_localization

conditioned-source-separation-lasaft

A PyTorch implementation of the paper: "LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation" (ICASSP 2021)

conditioned-u-net

Conditioned U-Net for Music Source Separation

cone-of-silence

The Cone of Silence:

conferencingspeech2021

Conferencing Speech Challenge

conv-tasnet

Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement

conv-tasnet-1

A PyTorch implementation of "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" (see recipes in aps framework https://github.com/funcwj/aps)

cpjku_dcase19

css_with_tstransformer

Code for the INTERSPEECH-2021 paper: Ultra Fast Speech Separation Model with Teacher Student Learning.

daily_arxiv

Using GitHub Action to collect paper list with publicly available source code in the daily arxiv

darcn

The implementation of "A Recursive Network with Dynamic Attention for Monaural Speech Enhancement"

dcase2020-task1

Jupyter notebook for DCASE 2020 challenge Task 1

dcase2020_code

dcase2020_task1

Code for DCASE 2020 task 1a and task 1b.

dcase2020_task1b

Initial

deep-learning-project-template

A best practice for deep learning project template architecture.

deepcomplexcrn

deepfilternet

Noise supression using deep filtering

deepxi

Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

demucs

Code for the paper Music Source Separation in the Waveform Domain

denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.

denoising-diffusion-pytorch

Implementation of Denoising Diffusion Probabilistic Model in Pytorch