Traditional approaches to the task of automatic classification of music by genre generally focus on the use of note-duration symbols to represent musical content. As well, most studies generally extract information from a combination of instruments. This paper compares the traditional method of using note-duration symbols with another method based on time slice representations of the music to s...

Music genre classification systems are normally build as a feature extraction module followed by a classifier. The features are often short-time features with time frames of 10-30ms, although several characteristics of music require larger time scales. Thus, larger time frames are needed to take informative decisions about musical genre. For the MIREX music genre contest several authors derive ...

We present a strategy to perform automatic genre classification of musical signals. The technique divides the signals into 21.3 milliseconds frames, from which 4 features are extracted. The values of each feature are treated over 1-second analysis segments. Some statistical results of the features along each analysis segment are used to determine a vector of summary features that characterizes ...

The Semantic Web has made it possible to automatically find meaningful connections between musical pieces which can be used to infer their degree of similarity. Similarity in turn, can be used by recommender systems driving music discovery or playlist generation. One useful facet of knowledge for this purpose are fine-grained genres and their inter-relationships. In this paper we present a meth...

In this paper we investigate social tags as a novel highvolume source of semantic metadata for music, using techniques from the fields of information retrieval and multivariate data analysis. We show that, despite the ad hoc and informal language of tagging, tags define a low-dimensional semantic space that is extremely well-behaved at the track level, in particular being highly organised by ar...

This automatic genre classification project can be divided into two steps. The first step is feature extraction. In [1], the author suggested several features which can be used to characterize the music, such as Time Domain Zero Crossings, Mel-Frequency Cepstral Coefficients, and Spectral Centroid and so on. In [2], Aucouturier and Pachet separated all music features into three sets: timbre rel...

In this project automatic genre classification problem will be studied and a new system for music genre will be developed. This paper focuses on impact of features on music genre classification task. The following genres are used in this project: Jazz, Metal, Hip-Hop, Classical and Disco, Reggae, Country, Pop. It is found that 3 features out of 26 performs better in genre classification task.

This paper presents the jWebMiner 2.0 cultural feature extraction software and describes the results of several musical genre classification experiments performed with it. jWebMiner 2.0 is an easy-to-use and open-source tool that allows users to mine the Internet in order to extract features based on both Last.fm social tags and general web search string co-occurrences extracted using the Yahoo...

Musical composition is a creative art, but is restricted by the limitations of the finite musical information that can be expressed. Though notation allows expressive qualities to be applied to notes, composition is limited within the realms of the octave; therefore only a limited number of combinations of musical notes are permitted within a measure or musical piece. This restraint combined wi...