Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

In this paper, we undertake a critical assessment of a stateof-the-art deep neural network
approach for computational rhythm analysis. Our methodology is to deconstruct this …

As an important format of multimedia, music has filled almost everyone's life. Automatic
analyzing of music is a significant step to satisfy people's need for music retrieval and music …

The guitar is a popular instrument for a variety of reasons, including its ability to produce
polyphonic sound and its musical versatility. The resulting variability of sounds, however …

Transformer is a successful deep neural network (DNN) architecture that has shown its
versatility not only in natural language processing but also in music information retrieval …

This paper discusses the application of convolutional neural networks (CNNs) to minimum
variance distortionless response localization schemes. We investigate the direction of arrival …

The online estimation of rhythmic information, such as beat positions, downbeat positions,
and meter, is critical for many real-time music applications. Musical rhythm comprises …

Automatic drum transcription is the process of generating symbolic notation for percussion
instruments within audio recordings. To date, recurrent neural network (RNN) systems have …

In this article we explore how the different semantics of spectrograms' time and frequency
axes can be exploited for musical tempo and key estimation using Convolutional Neural …