Slizovskaia O, Gómez E, Haro G. A Case Study of Deep-Learned Activations via Hand-Crafted Audio Features. The 2018 Joint Workshop on Machine Learning for Music. Joint workshop program of ICML, IJCAI/ECAI, and AAMAS
We develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:
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Artificial Intelligence |
Nonlinear Time Series Analysis |
Web Research |
Music Technology |
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Interactive Technologies |
Barcelona MedTech |
Natural Language Processing |
Nonlinear Time Series Analysis |
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UbicaLab |
Wireless Networking |
Educational Technologies |
Slizovskaia O, Gómez E, Haro G. A Case Study of Deep-Learned Activations via Hand-Crafted Audio Features. The 2018 Joint Workshop on Machine Learning for Music. Joint workshop program of ICML, IJCAI/ECAI, and AAMAS
Slizovskaia O, Gómez E, Haro G. A Case Study of Deep-Learned Activations via Hand-Crafted Audio Features. The 2018 Joint Workshop on Machine Learning for Music. Joint workshop program of ICML, IJCAI/ECAI, and AAMAS
This work presents a method for analysis of the activations of audio convolutional neural networks by use of hand-crafted audio features. We analyse activations from three CNN architectures trained on different datasets and compare shallow-level activation maps with harmonic-percussive source separation and chromagrams, and deep-level activations with loudness and onset rate
arXiv postprint: https://arxiv.org/abs/1907.01813
https://sites.google.com/site/faimmusic2018/home