Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications

A library for audio and music analysis, feature extraction.

Audio feature extraction for JavaScript.

Novoic's audio feature extraction library

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].

LibXtract is a simple, portable, lightweight library of audio feature extraction functions.

Audio feature extraction and classification

A collection of audio feature extraction algorithms written in the Vamp plugin format.

A simple audio feature extraction library

SoundPy (alpha stage) is a research-based python package for speech and sound. Applications include deep-learning, filtering, speech-enhancement, audio augmentation, feature extraction and visualization, dataset and audio file conversion, and beyond.

The SDK for Vamp plugins, an API for audio analysis and feature extraction plugins.

AENet: audio feature extraction

We'll look into audio categorization using deep learning principles like Artificial Neural Networks (ANN), 1D Convolutional Neural Networks (CNN1D), and CNN2D in this repository. We undertake some basic data preprocessing and feature extraction on audio sources before developing models. As a result, the accuracy, training time, and prediction time of each model are compared. This is explained by model deployment, which allows users to load the desired sound output for each model that is successfully deployed, as will be addressed in more depth later.

Spectral audio feature extraction using time-frequency reassignment

Real-time audio analysis library, support acoustic feature extraction and real-time beats detection

Batch tool for feature extraction and annotation of audio files using Vamp plugins

acoss: Audio Cover Song Suite is a framework for feature extraction and benchmarking for the cover song identification (CSI) task

A notebook analyzing different content based features in an audio file.

Vamp audio feature extraction plugins from the Centre for Digital Music at Queen Mary, University of London.

Cross platform audio feature extraction and sound classification tool

This repository contains CryMLClassifier, a machine learning model that classifies baby cries into five categories. It utilizes 193 features extracted from the cry audio data, achieving high accuracy with Random Forest and XGBoost algorithms. The repository includes a "features_extraction" folder for feature extraction code samples.

A comprehensive, AI-powered document and audio extraction platform that supports multiple extraction engines for PDFs, images, and audio files. Built with FastAPI (backend) and Next.js 14 (frontend), featuring real-time processing, user management, and project organization.

This may become an audio feature extraction library for Rust.

Audio/music feature extraction using Librosa in Python

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Audio feature extraction and multi-classification with the ECS-10 data set

CAMEL (Content-based Audio and Music Extraction Library) is an easy-to-use C++ framework developed for content-based audio and music analysis. The framework provides a set of tools for easy Segmentation, Feature Extraction, Domain Extraction, etc.

A self-supervised method for feature extraction from audio.

Feature Extractor is a real-time audio feature extraction tool. It can analyse audio on multiple input tracks in parallel.

An audio feature extraction library for the JUCE framework. Porting of wbrent/timbreID (original Pure Data external) .