linan2

TensorFlow-speech-enhancement-Chinese

基于深度学习的语音增强、去混响

Voice-activity-detection-VAD-paper-and-code

Voice activity detection (VAD) paper and code（From 198*~ ）and its classification.

TensorFlow-speech-enhancement

DNN and RCED speech enhancement

VAD_MATLAB

A simple VAD method

add_reverb2

Data augment. Add reverb and noise in speech.

Decision-tree

K236 task

group12

tju_12

linan2

Config files for my GitHub profile.

pytorch-dialect-speech-classification

pytorch-dialect-speech-classification

A-Convolutional-Recurrent-Neural-Network-for-Real-Time-Speech-Enhancement

A minimum unofficial implementation of the "A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement" (CRN) using PyTorch

Conv-TasNet-PyTorch

A PyTorch implementation of Conv-TasNet

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].

tensorflow-1.4.0

TensorFlow 1.4.0 installed version.

Tutorial_Separation

This repo summarizes the tutorials, datasets, papers, codes and tools for speech separation and speaker extraction task. You are kindly invited to pull requests.

Wave-U-Net-for-Speech-Enhancement

Implement Wave-U-Net by PyTorch, and migrate it to the speech enhancement.

-

acad-homepage.github.io

AcadHomepage: A Modern and Responsive Academic Personal Homepage

add_reverb

awesome-speech-enhancement

speech enhancement\speech seperation\sound source localization

beamforming

BSS_COLEGRAM

ICA_NMF_JADE

DNN_Localization_And_Separation

Speech Localization and Separation using DNNs

espnet

End-to-End Speech Processing Toolkit

FinalYearProject

Speech Enhancement using KF

mic_array

DOA, VAD and KWS for ReSpeaker Microphone Array

nara_wpe

Different implementations of "Weighted Prediction Error" for speech dereverberation

rsrgan

Robust Speech Recognition Using Generative Adversarial Networks (GAN)

TJUThesis_master_2021

天大博士/硕士学位论文Latex模板，根据2021年版要求修改，可直接在Overleaf上运行。:star:所写的论文成功提交天津大学图书馆存档！（2021.12.24）

Waveforms-Speech-Enhancement-Use-TensorFlow