The GTZAN dataset appears in at least 100 published works, and is the most-used public
dataset for evaluation in machine listening research for music genre recognition (MGR). Our …

Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic
data, and other modalities. While reviews have been written of some of this work before, no …

Recent research work has shown that the magnitude spectrogram of a song can be
considered as a superposition of a low-rank component and a sparse component, which …

There has been an increasing attention on learning feature representations from the
complex, high-dimensional audio data applied in various music information retrieval (MIR) …

Digital music has become prolific in the web in recent decades. Automated recommendation
systems are essential for users to discover music they love and for artists to reach …

We propose a convolutional neural network architecture with k-max pooling layer for
semantic modeling of music. The aim of a music model is to analyze and represent the …

Nos dias atuais há uma quantidade massiva de dados textuais sendo produzida e
armazenada diariamente na forma de e-mails, relatórios, artigos e postagens em redes …

Movies are one of the most prominent means of entertainment. The widespread use of the
Internet in recent times has led to large volumes of data related to movies being generated …

This paper concerns the development of a music dictionary-based model for summarizing
local feature descriptors computed over time. Comparing to a holistic representation, this text …

This paper presents a novel feature representation called sparse cepstral codes for
instrument identification. We first motivate the approach by discussing why cepstrum is …