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pulseaudio-nokia

Nokia pulseaudio blobs repo

python-for-signal-processing

Notebooks for "Python for Signal Processing" book

pytorch-stargan-vc

Fully reproduce the paper of StarGAN-VC. Stable training and Better audio quality .

real-time-voice-cloning

Clone a voice in 5 seconds to generate arbitrary speech in real-time

rnnoise

Recurrent neural network for audio noise reduction

rockbox

Read-only mirror of rockbox' main repository

silk-sdk

node-gyp version of Silk Speech Codec, able to decode/encode audio from/to silk format (widely used by Tencent apps, such as WeChat/WeiXin, QQ)

so-vits-svc

SoftVC VITS Singing Voice Conversion

sof-18-04-55-version

sof

sound-open-firmware

source_separation

Deep learning based speech source separation using Pytorch

speech-emotion-analyzer

The neural network model is capable of detecting five different male/female emotions from audio speeches. (Deep Learning, NLP, Python)

speech-enhancement

Deep learning for audio denoising

speech-enhancement-1

Deep neural network based speech enhancement toolkit

speech_signal_processing_and_classification

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

stargan-voice-conversion

full tensorflow implementation of the paper: StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks https://arxiv.org/abs/1806.02169

tensorflow

An Open Source Machine Learning Framework for Everyone

ugatit

Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)

voice-based-gender-recognition

Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)

voice-changer

リアルタイムボイスチェンジャー Realtime Voice Changer

voice-gender

Gender recognition by voice and speech analysis

wx-voice

Convert audio files between Tencent apps (Weixin / Wechat, QQ) and Silk codec with other general formats such as MP3 and M4A

xjia520.github.io

zero_shot_audio_source_separation

The official code repo for "Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data", in AAAI 2022