104 Open source projects that are alternatives of or similar to wavelet-denoiser

HiFi-GAN: High Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks

A curated list of awesome Speech Enhancement papers, libraries, datasets, and other resources.

PyTorch implementation of "A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

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

A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.

Emotion recognition by speech in android.

A React-Native Bridge for the Google Dialogflow (API.AI) SDK

Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

Complex Continuous Wavelet Transform

Novoic's audio feature extraction library

Collection of EM algorithms for blind source separation of audio signals

A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.

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

Implementation of Neural Voice Cloning with Few Samples Research Paper by Baidu

ConvNets for Audio Recognition using Google Commands Dataset

Wavelet CNN, Texture Classification in Keras

iOS application for finding formants in spoken sounds

VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network

SincNet is a neural architecture for efficiently processing raw audio samples.

Text-to-Speech Toolkit of the Speech and Language Technologies Group at the University of Stuttgart. Objectives of the development are simplicity, modularity, controllability and multilinguality.

Speech recognition toolkit for the arduino

RETIRED - OpenSTT is now retired. If you would like more information on Mycroft AI's open source STT projects, please visit:

A python wrapper for Speech Signal Processing Toolkit (SPTK).

Analytic signal-based source information analysis for YANGstraight and real-time interactive tools

Tracking the progress in end-to-end speech translation

PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

(1st place at HopHacks) A dynamic webVR memory palace for speech training, utilizing natural language processing and Google Streetview API

Deep Learning-based Voice Conversion system

Real-time GCC-NMF Blind Speech Separation and Enhancement

A light weight neural speaker embeddings extraction based on Kaldi and PyTorch.

An experiment for building gateware for the axiom micro / beta using nmigen and yosys

A modified version of Speech Signal Processing Toolkit (SPTK)

PyTorch implementation of VQ-VAE + WaveNet by [Chorowski et al., 2019] and VQ-VAE on speech signals by [van den Oord et al., 2017]

Discriminative Neural Clustering for Speaker Diarisation

Differentiable fast wavelet transforms in PyTorch with GPU support.

Keras (tensorflow) implementation of SincNet (Mirco Ravanelli, Yoshua Bengio - https://github.com/mravanelli/SincNet)

Deep neural network based speech enhancement toolkit

A implementation of Power Normalized Cepstral Coefficients: PNCC

The official network explorer for Wavelet.

Natural Language Processing to generate new speeches for the President of Turkey.

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].

Open source audio annotation tool for humans™

Aydin — User-friendly, Fast, Self-Supervised Image Denoising for All.

A neural network for end-to-end speech denoising

Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.

Problem Agnostic Speech Encoder

External Module for ITK, implementing Isotropic Wavelets and Riesz Filter for multiscale phase analysis.

Library to build speech synthesis systems designed for easy and fast prototyping.

WaveNet vocoder

This repository has implementation for "Neural Voice Cloning With Few Samples"

A data annotation pipeline to generate high-quality, large-scale speech datasets with machine pre-labeling and fully manual auditing.

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

a neural network toolbox for animal vocalizations and bioacoustics

NHW : A Next-Generation Image Compression Codec

[wip]Speech recognition tool-box written by Nim. Based on Arraymancer.

Tracking the progress in non-autoregressive generation (translation, transcription, etc.)

Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland. - Mirrored from https://gitlab.idiap.ch/bob/bob

Discover, install, and share napari plugins

Statistical Machine Intelligence & Learning Engine

The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others.