Machine learning, in numpy

a library for audio and music analysis

A C++ Library for Audio Analysis

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].

.NET library for 1D signal processing focused specifically on audio processing

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

Detecting emotions using MFCC features of human speech using Deep Learning

A program for automatic speaker identification using deep learning techniques.

Synchronize your subtitles using machine learning

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

A simple audio feature extraction library

python codes to extract MFCC and FBANK speech features for Kaldi

A implementation of Power Normalized Cepstral Coefficients: PNCC

Lyrics-to-audio-alignement system. Based on Machine Learning Algorithms: Hidden Markov Models with Viterbi forced alignment. The alignment is explicitly aware of durations of musical notes. The phonetic model are classified with MLP Deep Neural Network.

aubio plugins for Vamp

SpeakerVoiceIdentifier can recognize the voice of a speaker by learning.

Live Audio MFCC Visualization in the browser using Web Audio API - https://pulakk.github.io/Live-Audio-MFCC/tutorial

This repository contains a Matlab class, a Python module, a Jupyter notebook, and a Julia module which implement/illustrate several methods/functions for audio signal processing.

A pytorch implementation of MFCC.

In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec (AMR-WB). In a subjective CCR listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to G.711, even en par with uncoded speech.

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

Python implementation of papers on Emergency Vehicle Detection

🔉 👦 👧 👩 👨 Speaker identification using voice MFCCs and GMM

FragIt main repository

Deep learning using CNN for Mandarin Chinese tone classification

A Python 2.7 implementation of Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW) algorithms for Automated Speech Recognition (ASR).

Basics of Musical Instruments Classification using Machine Learning

Using MFCC feature and DTW algorithm to recognize rumber 0-9

Extract mfcc vectors and phones from TIMIT dataset