Speech is a powerful medium of communication that always convey rich and useful
information, such as gender, accent, and other unique characteristics of a speaker. These …

The music domain is among the most important ones for adopting recommender systems
technology. In contrast to most other recommendation domains, which predominantly rely on …

The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-
temporal patterns makes them well suited to environmental sound classification. However …

This document describes version 0.4. 0 of librosa: a Python package for audio and music
signal processing. At a high level, librosa provides implementations of a variety of common …

The Internet of Musical Things (IoMusT) is an emerging research field positioned at the
intersection of Internet of Things, new interfaces for musical expression, ubiquitous music …

While deep learning has enabled great advances in many areas of music, labeled music
datasets remain especially hard, expensive, and time-consuming to create. In this work, we …

Listening to music often evokes intense emotions [1, 2]. Recent research suggests that
musical pleasure comes from positive reward prediction errors, which arise when what is …

The design of meaningful audio features is a key need to advance the state-of-the-art in
music emotion recognition (MER). This article presents a survey on the existing emotionally …

Openly available datasets are a key factor in the advancement of data-driven research
approaches, including many of the ones used in sound and music computing. In the last few …

Sound event detection (SED) in environmental recordings is a key topic of research in
machine listening, with applications in noise monitoring for smart cities, self-driving cars …