Music recommender systems (MRSs) have experienced a boom in recent years, thanks to
the emergence and success of online streaming services, which nowadays make available …

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

This paper investigates the use of automatically extracted visual features of videos in the
context of recommender systems and brings some novel contributions in the domain of …

Music genres allow to categorize musical items that share common characteristics. Although
these categories are not mutually exclusive, most related research is traditionally focused on …

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 …

Since musical genre is one of the most common ways used by people for managing digital
music databases, music genre recognition is a crucial task, deep studied by the Music …

Modern recommendation systems require access to and understanding of the big data built
on top of large data islands. This is important as the growing enhancement in …

We propose a novel approach to context-aware music recommendation-recommending
music suited for places of interest (POIs). The suggested hybrid approach combines two …

In this paper a novel and effective approach for automated audio classification is presented
that is based on the fusion of different sets of features, both visual and acoustic. A number of …

The remarkable success of deep convolutional neural networks in image-related
applications has led to their adoption also for sound processing. Typically the input is a time …