Essentia TensorFlow Models For Audio And Music Processing On The Web

Web Audio Conference 2021 - Online - July 5-7 https://webaudioconf2021.com/paper-c-4/ Albin Correya (Universitat Pompeu Fabra); Pablo Alonso-Jiménez (Universitat Pompeu Fabra); Jorge Marcos-Fernández (Universitat Pompeu Fabra); Xavier Serra (Universitat Pompeu Fabra ); Dmitry Bogdanov (Universitat Pompeu Fabra)* Abstract: Recent advances in web-based machine learning (ML) tools empower a wide range of application developers in both industrial and creative contexts. The availability of pre-trained ML models and JavaScript (JS) APIs in frameworks like TensorFlow.js enabled developers to use AI technologies without demanding domain expertise. Nevertheless, there is a lack of pre-trained models in web audio compared to other domains, such as text and image analysis. Motivated by this, we present a collection of open pre-trained TensorFlow.js models for music-related tasks on the Web. Our models currently allow for different types of music classification (e.g., genres, moods, danceability, voice or instrumentation), tempo estimation, and music feature embeddings. To facilitate their use, we provide a dedicated JS add-on module essentia.js-model within the Essentia.js library for audio and music analysis. It has a simple API, enabling end-to-end analysis from audio input to prediction results on web browsers and Node.js. Along with the Web Audio API and web workers, it can also be used to build real-time applications. We provide usage examples, discuss possible use-cases, and report benchmarking results. Intro music by Caucenus - soundcloud.com/caucenus

Видео Essentia TensorFlow Models For Audio And Music Processing On The Web автора JavaScript Developer