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 …

Following their success in Computer Vision and other areas, deep learning techniques have
recently become widely adopted in Music Information Retrieval (MIR) research. However …

We present a content-based automatic music tagging algorithm using fully convolutional
neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional …

In this paper, we present a transfer learning approach for music classification and regression
tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using …

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 …

A common criticism of deep learning relates to the difficulty in understanding the underlying
relationships that the neural networks are learning, thus behaving like a black-box. In this …

A number of recent advances in neural audio synthesis rely on up-sampling layers, which
can introduce undesired artifacts. In computer vision, upsampling artifacts have been …

This paper presents “BachBot”: an end-to-end automatic composition system for composing
and completing music in the style of Bach's chorales using a deep long short-term memory …

Musical performance combines a wide range of pitches, nuances, and expressive
techniques. Audio-based classification of musical instruments thus requires to build signal …

Deep convolutional neural networks (CNNs) have been actively adopted in the field of music
information retrieval, eg genre classification, mood detection, and chord recognition …