Music genre recognition based on visual representation has been successfully explored
over the last years. Classifiers trained with textural descriptors (eg, Local Binary Patterns …

In this paper we present an approach to music genre classification which converts an audio
signal into spectrograms and extracts texture features from these time-frequency images …

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 …

This paper proposes a 1D residual convolutional neural network (CNN) architecture for
music genre classification and compares it with other recent 1D CNN architectures. The 1D …

In this paper, we propose a parallel Convolutional Neural Network architecture for the task of
classifying acoustic scenes and urban sound scapes. A popular choice for input to a …

In this paper we present an alternative approach for music genre classification which
converts the audio signal into spectrograms and then extracts features from this visual …

One of the goals of the music information retrieval (MIR) community is to research new
methods and create new systems that can efficiently and effectively retrieve and recommend …

Movie genre classification is a challenging task that has increasingly attracted the attention
of researchers. The number of movie consumers interested in taking advantage of automatic …

In this paper we investigate performance differences of different neural network architectures
on the task of automatic music genre classification. Comparative evaluations on four well …

ABSTRACT The Million Song Dataset (MSD), a collection of one million music pieces,
enables a new era of research of Music Information Retrieval methods for large-scale …