Tensorflow Audio Models in Essentia
Channel: MusicTechnologyGroup
Date: 07/05/2020

Presentation by Pablo Alonso-Jiménez at ICASSP2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing.

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.

Publication: https://ieeexplore.ieee.org/document/9054688
Authors: Pablo Alonso-Jiménez, Dmitry Bogdanov, Jordi Pons, Xavier Serra