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manyears

ManyEars Sound Source Localization, Tracking and Separation

meta-tasnet

A PyTorch implementation of Meta-TasNet from "Meta-learning Extractors for Music Source Separation

ml-kws-for-mcu

ml-sound-classifier

Machine Learning Sound Classifier

mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

model-compression

model compression based on pytorch (1、quantization: 16/8/4/2 bits(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net)；2、 pruning: normal、regular and group convolutional channel pruning；3、 group convolution structure；4、batch-normalization folding for quantization)

ms-snsd

The Microsoft Scalable Noisy Speech Dataset (MS-SNSD) is a noisy speech dataset that can scale to arbitrary sizes depending on the number of speakers, noise types, and Speech to Noise Ratio (SNR) levels desired.

mtf-crnn

Inspired by the convolutional recurrent neural network(CRNN) and inception, we propose a multiscale time-frequency convolutional recurrent neural network (MTF-CRNN) for audio event detection. Our goal is to improve audio event detection performance and recognize target audio events that have different lengths and accompany the complex audio background. We exploit multi-groups of parallel and serial convolutional kernels to learn high-level shift invariant features from the time and frequency domains of acoustic samples. A two-layer bi-direction gated recurrent unit) based on the recurrent neural network is used to capture the temporal context from the extracted high-level features. The proposed method is evaluated on the DCASE2017 challenge dataset. Compared to other methods, the MTF-CRNN achieves one of the best test performances for a single model without pre-training and without using a multi-model ensemble approach.

multi-channel-speech-extraction-using-dnn

A tensorflow implementation of my paper Combining beamforming and deep neural networks for multi-channel speech extraction

music-source-separation

Separating singing voice from music based on deep neural networks in Tensorflow

music_source_separation

musicnn

Pronounced as "musician", musicnn is a set of pre-trained deep convolutional neural networks for music audio tagging.

mvision

机器人视觉 无人驾驶 视觉SLAM ORB LSD SVO DSO 深度学习目标检测yolov3 行为检测 opencv PCL 双目视觉

nara_wpe

Different implementations of "Weighted Prediction Error" for speech dereverberation

ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

nisqa

NISQA - Non-Intrusive Speech Quality Assessment

nn-irm

A Simple DNN-IRM estimator for speech enhancement

nnaudio

Audio processing by using pytorch 1D convolution network

nndl.github.io

《神经网络与深度学习》 Neural Network and Deep Learning

nnom

A higher-level Neural Network library for microcontrollers.

noise-reduction-using-rnn

Implements python programs to train and test a Recurrent Neural Network with Tensorflow

nussl

A simple audio source separation library built in python

onssen

An open-source speech separation and enhancement library

openmha

The open Master Hearing Aid (openMHA)

opensmile

A github repo of the openSMILE feature extraction tool.

paderwasn

Paderwasn is a collection of methods for acoustic signal processing in wireless acoustic sensor networks (WASNs).

panotti

A multi-channel neural network audio classifier using Keras

pedalboard

A Python library for adding effects to audio.

percepnet

(Work In Progress) Unofficial implementation of PercepNet: A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech

pit-speech-separation