In this paper, we present nnAudio, a new neural network-based audio processing framework
with graphics processing unit (GPU) support that leverages 1D convolutional neural …

Creating novel interpretations of existing musical compositions is and has always been an
essential part of musical practice. Before the advent of recorded music, listening to a piece of …

The version identification (VI) task deals with the automatic detection of recordings that
correspond to the same underlying musical piece. Despite many efforts, VI is still an open …

In the field of music information retrieval (MIR), cover song identification (CSI) is a
challenging task that aims to identify cover versions of a query song from a massive …

We present in this paper ByteCover, which is a new feature learning method for cover song
identification (CSI). Byte-Cover is built based on the classical ResNet model, and two major …

Cover song identification is a challenging task in the field of Music Information Retrieval
(MIR) due to complex musical variations between query tracks and cover versions. Previous …

This paper deals with a score–audio music retrieval task where the aim is to find relevant
audio recordings of Western classical music, given a short monophonic musical theme in …

Automatic cover detection-the task of finding in an audio dataset all covers of a query track-
has long been a challenging theoretical problem in MIR community. It also became a …

This chapter provides an overview of how deep learning techniques can be used for audio
signals. We first review the main DNN architectures, meta-architectures and training …

Recent works have addressed the automatic cover detection problem from a metric learning
perspective. They employ different input representations, aiming to exploit melodic or …