Manifold approaches exist for organization of music by genre and/or style. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word occurrences on related pages. To classify artists we primarily use support vector machines. We present 3 experiments in which we address the following issues. First, we study the performance of our approach compared to previous work. Second, we investigate how daily fluctuations in the Internet affect our approach. Third, on a set of 224 artists from 14 genres we study (a) how many artists are necessary to define the concept of a genre, (b) which search engines perform best, (c) how to formulate search queries best, (d) which overall performance we can expect for classification, and finally (e) how our approach is suited as a similarity measure for artists.

Abstract — This paper presents a novel approach to the task of automatic music genre classification which is based on multiple feature vectors and ensemble of classifiers. Multiple feature vectors are extracted from a single music piece. First, three 30-second music segments, one from the beginning, one from the middle and one from end part of a music piece are selected and feature vectors are extracted from each segment. Individual classifiers are trained to account for each feature vector extracted from each music segment. At the classification, the outputs provided by each individual classifier are combined through simple combination rules such as majority vote, max, sum and product rules, with the aim of improving music genre classification accuracy. Experiments carried out on a large dataset containing more than 3,000 music samples from ten different Latin music genres have shown that for the task of automatic music genre classification, the features extracted from the middle part of the music provide better results than using the segments from the beginning or end part of the music. Furthermore, the proposed ensemble approach, which combines the multiple feature vectors, provides better accuracy than using single classifiers and any individual music segment. I.

Abstract. We present an algebra whose operations act on musical pieces, and show how this algebra can be used to generate music in a tree-based fashion. Starting from input which is either generated by a regular tree grammar or provided by the user via a digital keyboard, a sequence of top-down tree transducers is applied to generate a tree over the operations provided by the music algebra. The evaluation of this tree yields the musical piece generated. 1

We develop a hidden Markov mixture model based on a Dirichlet process (DP) prior, for represen-tation of the statistics of sequential data for which a single hidden Markov model (HMM) may not be sufficient. The DP prior has an intrinsic clustering property that encourages parameter sharing, and this naturally reveals the proper number of mixture components. The evaluation of posterior distributions for all model parameters is achieved in two ways: (i) via a rigorous Markov chain Monte Carlo method, and (ii) approximately and efficiently via a variational Bayes formulation. Using DP HMM mixture models in a Bayesian setting, we propose a novel scheme for music analysis, highlighting the effectiveness of the DP HMM mixture model. Music is treated as a time-series data sequence and each music piece is represented as a mixture of HMMs. We approximate the similarity of two music pieces by computing the distance between the associated HMM mixtures. Experimental results are presented for synthesized sequential data and from classical music clips. Music similarities computed using DP HMM mixture modeling are compared to those computed from Gaussian mixture modeling, for which the mixture modeling is also performed using DP. The results show that the performance of DP HMM mixture modeling exceeds that of the DP GMM modeling.

This paper presents a novel approach to the task of automatic music genre classification which is based on ensemble learning. Feature vectors are extracted from three 30-second music segments from the beginning, middle and end of each music piece. Individual classifiers are trained to account for each music segment. During classification, the output provided by each classifier is combined with the aim of improving music genre classification accuracy.

The organization of music is one of the central challenges in times of increasing distribution of digital music. A well-tried means is the classification in genres and/or styles. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word occurrences on related pages. To classify artists we primarily use support vector machines. Based on a previously published paper and on a master’s thesis, we present experiments comprising the evaluation of the classification process on a taxonomy of 14 genres with altogether 224 artists, as well as an estimation of the impact of daily fluctuations in the Internet on our approach, exploiting a long-term study over a period of almost one year. On the basis of these experiments we study (a) how many artists are necessary to define the concept of a genre, (b) which search engines perform best, (c) how to formulate search queries best, (d) which overall performance we can expect for classification, and finally (e) how our approach is suited as a similarity measure for artists.

Data Mining is a powerful technology nowadays to discover and analyze large data sets. It has its applications in various fields of Arts, Science and Engineering. One such field is Music. Music is a form of arts which comes under fine arts category. It may be melody or rhythmic. It is broadly categorized as western music and classical music. Carnatic music is a form of south Indian classical music which comprises of swarams (7 notes) to evolve music. This research work deals with automatic identification of Carnatic raga Swaram notes through Data Mining algorithms. The training sets considered for the work are Avarohanam notes of 72 melakartha raga and 212 Janya raga. C4.5 decision tree algorithm, Random Tree and Rule Induction algorithm were utilized to classify the Melakartha raga and the Janya raga. However the Janya raga swaram notes were also investigated through the use of appropriate feature relevance algorithms namely Feature ranking, Correlation based Feature Selection (CFS) filtering, and Fast Correlation based Filter (FCBF) filtering. The Melakartha raga data set was accurately classified with 100% accuracy by all the aforementioned classification techniques while predictor attributes selected through feature ranking algorithm produced nearly 90 % accurate classification with Rule Induction algorithm on the Janya raga data.