In this paper we present beat histogram features for multiple level rhythm description and evaluate them in a musical genre classification task. Audio features pertaining to various musical content categories and their related novelty functions are extracted as a basis for the creation of beat histograms. The proposed features capture not only amplitude, but also tonal and general spectral chan...

We propose a generative model for melodies in a given musical genre, using symbolic representation of musical data. We first compute melodic features that represent the plausibility of sequences of three consecutive notes. Their probabilistic modeling is an interesting intermediate problem since the cardinality of such features is much lower than the number of sequences of three notes. We then ...

Community detection methods from complex network theory are applied to a subset of the Myspace artist network to identify groups of similar artists. Methods based on the greedy optimization of modularity and random walks are used. In a second iteration, inter-artist audio-based similarity scores are used as input to enhance these community detection methods. The resulting community structures a...

Applications of Convolutional Neural Networks (CNNs) to various problems have been the subject of a number of recent studies ranging from image classification and object detection to scene parsing, segmentation 3D volumetric images and action recognition in videos. CNNs are able to learn input data representation, instead of using fixed engineered features. In this study, the image model traine...

Automatic music genre classification is one of the most challenging problems in music information retrieval and management of digital music database. In this paper, we propose a new method to classify music genres using text categorization methods. Differing from previous solutions which were mainly based on analysis on acoustic or symbolic audio signal, here we consider music as a text-like se...

We can see many and strong links between music and human body movement in musical performance, in dance, and in the variety of movements that people make in listening situations. There is evidence that sensations of human body movement are integral to music as such, and that sensations of movement are efficient carriers of information about style, genre, expression, and emotions. The challenge ...

Organising or browsing music collections in a musically meaningful way calls for tagging the data in terms of e.g. rhythmic, melodic or harmonic aspects, among others. In some cases, such metadata can be extracted automatically from musical files; in others, a trained listener must extract it by hand. In this article, we consider a specific set of rhythmic descriptors for which we provide proce...

The emotional response to synchronous instances between musical and visual elements within the cinematic experience awaits thorough empirical evaluation. Film composition is defined here as a genre of stereotypes, whose methodologies are not feasibly subject to redevelopment. As consequence, the research focuses on improving the enculturated functions of film music. Hypothesising that an increa...

Automatic musical genre classification is an important tool for organizing the large collections of music that are becoming available to the average user. In addition it provides a structured way of evaluating musical content features that doesn’t require extensive user studies. This paper provides a detailed comparative analysis of various factors affecting automatic classification performance...

The number of studies investigating automated genre classification is growing following the increasing amounts of digital audio data available. The underlying techniques to perform automated genre classification in general include feature extraction and classification. In this study, MARSYAS was used to extract audio features and the suite of tools available in WEKA was used for the classificat...