Pinned Repositories
Audio-auto-tagging
Convolutional Neural Network for auto-tagging of audio clips on MagnaTagATune dataset
audioset_tagging_cnn
Auralisation
Auralisation of learned features in CNN (for audio)
BTC-ISMIR19
"A Bi-Directional Transformer for Musical Chord Recognition" accepted on ISMIR2019
ChangeVoice
NDK语音消息的变声处理
deep-learning-HAR
Convolutional and LSTM networks to classify human activity
hosts
:statue_of_liberty:最新可用的google hosts文件。国内镜像：
hosts-1
镜像：https://coding.net/u/scaffrey/p/hosts/git
ICASSP19
magenta
Magenta: Music and Art Generation with Machine Intelligence
blues-green's Repositories
blues-green/BTC-ISMIR19
"A Bi-Directional Transformer for Musical Chord Recognition" accepted on ISMIR2019
blues-green/Audio-auto-tagging
Convolutional Neural Network for auto-tagging of audio clips on MagnaTagATune dataset
blues-green/audioset_tagging_cnn
blues-green/Auralisation
Auralisation of learned features in CNN (for audio)
blues-green/ChangeVoice
NDK语音消息的变声处理
blues-green/deep-learning-HAR
Convolutional and LSTM networks to classify human activity
blues-green/hosts
:statue_of_liberty:最新可用的google hosts文件。国内镜像：
blues-green/hosts-1
镜像：https://coding.net/u/scaffrey/p/hosts/git
blues-green/ICASSP19
blues-green/magenta
Magenta: Music and Art Generation with Machine Intelligence
blues-green/MidiNet
This repository contains the source code of MdidNet : A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
blues-green/MSD_split_for_tagging
blues-green/musegan
An AI for Music Generation
blues-green/music_dataset_split
blues-green/musicautobot
Using deep learning to generate music in MIDI format.
blues-green/ontts
科大讯飞语音linux在线语音合成后台服务
blues-green/panotti
A multi-channel neural network audio classifier using Keras
blues-green/pretty-midi
Utility functions for handling MIDI data in a nice/intuitive way.
blues-green/pyenv-win
pyenv for Windows. pyenv is a simple python version management tool. It lets you easily switch between multiple versions of Python. It's simple, unobtrusive, and follows the UNIX tradition of single-purpose tools that do one thing well.
blues-green/python-Speech_Recognition
A simple example for use speech recognition baidu api with python.
blues-green/pyvad
VAD(Voice Activity Detector) python 实现对时时读入的流式数据进行端点检测
blues-green/remi
"Pop Music Transformer: Generating Music with Rhythm and Harmony", arXiv 2020
blues-green/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].
blues-green/spider163
抓取网易云音乐热门评论
blues-green/transformers
🤗 Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.
blues-green/VGG16CAM-keras
Keras implementation of the VGG16-CAM model
blues-green/XunFeiDemo
本Demo是科大讯飞SDK的简单Demo，展示了语音识别和语音合成功能的使用。