TensorFlow Audio Models in Essentia

• URL: http://arxiv.org/abs/2003.07393v1
• Date: Mon, 16 Mar 2020 18:23:30 GMT
• Title: TensorFlow Audio Models in Essentia
• Authors: Pablo Alonso-Jim\'enez, Dmitry Bogdanov, Jordi Pons, Xavier Serra
• Abstract summary: We present a set of algorithms that employ in Essentia. Essentia is a reference open-source C++/Python library for audio and music analysis.
• Score: 28.324123632999527
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions with pre-trained deep learning models, and are designed to offer flexibility of use, easy extensibility, and real-time inference. To show the potential of this new interface with TensorFlow, we provide a number of pre-trained state-of-the-art music tagging and classification CNN models. We run an extensive evaluation of the developed models. In particular, we assess the generalization capabilities in a cross-collection evaluation utilizing both external tag datasets as well as manual annotations tailored to the taxonomies of our models.

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