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 …

The lack of data tends to limit the outcomes of deep learning research, particularly when
dealing with end-to-end learning stacks processing raw data such as waveforms. In this …

Recently, the end-to-end approach that learns hierarchical representations from raw data
using deep convolutional neural networks has been successfully explored in the image, text …

Convolutional Neural Networks (CNN) have been applied to diverse machine learning tasks
for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw …

Music genre labels are useful to organize songs, albums, and artists into broader groups
that share similar musical characteristics. In this work, an approach to learn and combine …

Convolutional Neural Networks (CNNs) have had great success in many machine vision as
well as machine audition tasks. Many image recognition network architectures have …

Music genres allow to categorize musical items that share common characteristics. Although
these categories are not mutually exclusive, most related research is traditionally focused on …

Music genre classification based on visual representation has been successfully explored
over the last years. Recently, there has been increasing interest in attempting convolutional …

Pronounced as" musician", the musicnn library contains a set of pre-trained musically
motivated convolutional neural networks for music audio tagging: https://github …

Music auto-tagging is often handled in a similar manner to image classification by regarding
the two-dimensional audio spectrogram as image data. However, music auto-tagging is …