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The focus of this work is to study how to efficiently tailor Convolutional Neural Networks (CNNs) towards learning timbre representations from log-mel magnitude spectrograms. We first review the… (More)
Comunicacio presentada al Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), celebrat el dia 16 de novembre de 2017 a Munic, Alemanya.
This paper presents a method for recognizing musical instruments in user-generated videos. Musical instrument recognition from music signals is a well-known task in the music information retrieval… (More)
This report provides a solution for the task 1 of DCASE 2017 challenge. We build two parallel audio scene classification systems – LightGBM and VGG-net. Their prediction scores are output… (More)
Comunicacio presentada a la 13th Sound and Music Computing Conference, celebrada el 31 d'agost de 2016 a Hamburg, Alemanya.
Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model? This paper presents an extension of the Wave-U-Net  model which allows… (More)
Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However,… (More)
This work aims at investigating cross-modal connections between audio and video sources in the task of musical instrument recognition. We also address in this work the understanding of the… (More)
The explainability of Convolutional Neural Networks (CNNs) is a particularly challenging task in all areas of application, and it is notably under-researched in music and audio domain. In this paper,… (More)