Proceedings ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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.
Alonso-Jiménez P, Bogdanov D, Pons J, Serra X. TensorFlow Audio Models in Essentia. In: AA. VV.. Proceedings ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1 ed. Barcelona: IEEE; 2020. p. 266-270.