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3d-convolutional-speaker-recognition

:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

algorithm-visualizer

:fireworks:Interactive Online Platform that Visualizes Algorithms from Code

amazon-chime-sdk-pstn-audio-voice-focus

argus-freesound

Kaggle | 1st place solution for Freesound Audio Tagging 2019

athena

an open-source implementation of sequence-to-sequence based speech processing engine

aubio

a library for audio and music analysis

audio-effects

Collection of audio effects plugins implemented from the explanations in the book "Audio Effects: Theory, Implementation and Application" by Joshua D. Reiss and Andrew P. McPherson.

audio-super-res

Audio super resolution using neural networks

audio_based_identification_beehive_states

bandwidth_extension

beamformit_matlab

A MATLAB implementation of CHiME4 baseline Beamformit

blog

http://blogs.msdn.com/b/matthew_van_eerde

bnpy

Bayesian nonparametric machine learning for Python

brouhaha-vad

Predicts the level of noise and reverberation on your audiofiles

clumsy

clumsy makes your network condition on Windows significantly worse, but in a controlled and interactive manner.

cnn-audio-denoiser

Tensorflow 2.0 implementation of the paper: A Fully Convolutional Neural Network for Speech Enhancement

code-of-learn-deep-learning-with-pytorch

This is code of book "Learn Deep Learning with PyTorch"

conferencingspeech2022

Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge in Online Conferencing Applications

conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

dcase2017-baseline-system

DCASE 2017 Baseline system

dccrn-with-various-loss-functions

DCCRN with various loss functions

deep_architecture_genealogy

Deep Learning Architecture Genealogy Project

deeplearning_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

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.

diart

Lightweight python library for streaming speaker diarization in real-time implemented in pytorch

dnn_wpe

dns-challenge

This repo contains the scripts, models and required files for the Interspeech 2020 Deep Noise Suppression (DNS) Challenge. We are open sourcing clean speech and noise files as well. Participants of this challenge will use the scripts from this repo to create data to train their noise suppressors. They will compare their method with our baseline noise suppressor and report the results.

emovoice

Build your own Real-time Speech Emotion Recognizer

eng-practices

Google's Engineering Practices documentation

fdntoolbox

Matlab Toolbox for Feedack Delay Networks (FDNs)