Displaying 1 to 13 from 13 results

essentia - C++ library for audio and music analysis, description and synthesis, including Python bindings

  •    Jupyter

Essentia is an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications. If you use example extractors (located in src/examples), or your own code employing Essentia algorithms to compute descriptors, you should be aware of possible incompatibilities when using different versions of Essentia.

awesome-deep-learning-music - List of articles related to deep learning applied to music

  •    TeX

By Yann Bayle (Website, GitHub) from LaBRI (Website, Twitter), Univ. Bordeaux (Website, Twitter), CNRS (Website, Twitter) and SCRIME (Website). The role of this curated list is to gather scientific articles, thesis and reports that use deep learning approaches applied to music. The list is currently under construction but feel free to contribute to the missing fields and to add other resources! To do so, please refer to the How To Contribute section. The resources provided here come from my review of the state-of-the-art for my PhD Thesis for which an article is being written. There are already surveys on deep learning for music generation, speech separation and speaker identification. However, these surveys do not cover music information retrieval tasks that are included in this repository.

fma - FMA: A Dataset For Music Analysis

  •    Jupyter

Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson, EPFL LTS2. The dataset is a dump of the Free Music Archive (FMA), an interactive library of high-quality, legal audio downloads. Below the abstract from the paper.

musicinformationretrieval.com - Instructional notebooks on music information retrieval.

  •    Jupyter

stanford-mir is now musicinformationretrieval.com. This repository contains instructional Jupyter notebooks related to music information retrieval (MIR). Inside these notebooks are Python code snippets that illustrate basic MIR systems.

madmom - Python audio and music signal processing library

  •    Python

Madmom is an audio signal processing library written in Python with a strong focus on music information retrieval (MIR) tasks. The library is internally used by the Department of Computational Perception, Johannes Kepler University, Linz, Austria (http://www.cp.jku.at) and the Austrian Research Institute for Artificial Intelligence (OFAI), Vienna, Austria (http://www.ofai.at).

meyda - Audio feature extraction for JavaScript.

  •    Javascript

Meyda is a Javascript audio feature extraction library. Meyda supports both offline feature extraction as well as real-time feature extraction using the Web Audio API. We wrote a paper about it, which is available here. Please see the documentation for setup and usage instructions.

Strugatzki - Algorithms for matching audio file similarities

  •    Scala

Strugatzki is a Scala library containing several algorithms for audio feature extraction, with the aim of similarity and dissimilarity measurements. They have been originally used in my live electronic piece "Inter-Play/Re-Sound", then successively in the tape piece "Leere Null", the sound installation "Writing Machine", and the tape piece "Leere Null (2)". (C)opyright 2011–2017 by Hanns Holger Rutz. All rights reserved. It is released under the GNU Lesser General Public License v2.1+ and comes with absolutely no warranties. To contact the author, send an email to contact at sciss.de.

ireal-reader - A Node JS module to read music files from iRealPro.

  •    Javascript

This is a Node JS module to read music files from iRealPro. Install the module with npm install ireal-reader. In your project read in the HTML or ireal://... url output from iRealPro.

HPSS - Harmonic/Percussive Sound Separation

  •    C++

The codes in this repository were used in the experiments of the following paper. For academic use, please cite the paper above.

SymbTr - Turkish Makam Music Symbolic Data Collection

  •    Python

SymbTr is a collection machine readable symbolic scores aimed at performing computational studies of Turkish Makam music. SymbTr is currently the biggest machine readable collection of Turkish makam music. The latest version of the SymbTr collection consists of 2200 pieces from 155 makams, 88 usuls, 56 forms, about 865.000 musical notes and 80 hours nominal playback time. The scores are selected from reliable sources that consists of musical pieces from Turkish art and folk music. Special care has been taken to select works covering a broad historical time span among the ones which are still performed in the contemporary practice.

spotifyr - R wrapper for Spotify's Web API

  •    R

spotifyr is a wrapper for pulling track audio features and other information from Spotify's Web API in bulk. By automatically batching API requests, it allows you to enter an artist's name and retrieve their entire discography in seconds, along with Spotify's audio features and track/album popularity metrics. You can also pull song and playlist information for a given Spotify User (including yourself!). The development version now includes functions from the geniusR package from Josiah Parry.

crepe - CREPE: A Convolutional REpresentation for Pitch Estimation -- pre-trained model (ICASSP 2018)

  •    Python

CREPE: A Convolutional Representation for Pitch Estimation Jong Wook Kim, Justin Salamon, Peter Li, Juan Pablo Bello. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018. We kindly request that academic publications making use of CREPE cite the aforementioned paper.