Automatic Genre Classification of Latin Music Using Ensemble of Classifiers

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. Experiments carried out on a dataset containing 600 music samples from two Latin genres (Tango and Salsa) have shown that for the task of automatic music genre classification, the features extracted from the middle and end music segments provide better results than using the beginning music segment. Furthermore, the proposed ensemble method provides better accuracy than using single classifiers and any individual segment

The Latin Music Database

In this paper we present the Latin Music Database, a novel database of Latin musical recordings which has been developed for automatic music genre classification, but can also be used in other music information retrieval tasks. The method for assigning genres to the musical recordings is based on human expert perception and therefore capture their tacit knowledge in the genre labeling process. We also present the ethnomusicology of the genres available in the database as it might provide important information for the analysis of the results of any experiment that employs the database

Towards an efficient prover for the C1 paraconsistent logic

The KE inference system is a tableau method developed by Marco Mondadori which was presented as an improvement, in the computational efficiency sense, over Analytic Tableaux. In the literature, there is no description of a theorem prover based on the KE method for the C1 paraconsistent logic. Paraconsistent logics have several applications, such as in robot control and medicine. These applications could benefit from the existence of such a prover. We present a sound and complete KE system for C1, an informal specification of a strategy for the C1 prover as well as problem families that can be used to evaluate provers for C1. The C1 KE system and the strategy described in this paper will be used to implement a KE based prover for C1, which will be useful for those who study and apply paraconsistent logics.Comment: 16 page

Automatic Music Genre Classification Using Ensemble of Classifiers

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

Feature Selection in Automatic Music Genre Classification

This paper presents the results of the application of a feature selection procedure to an automatic music genre classification system. The classification system is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end of the original music signal (timedecomposition). Despite being music genre classification a multi-class problem, we accomplish the task using a combination of binary classifiers, whose results are merged in order to produce the final music genre label (space decomposition). As individual classifiers several machine learning algorithms were employed: Naive-Bayes, Decision Trees, Support Vector Machines and Multi-Layer Perceptron Neural Nets. Experiments were carried out on a novel dataset called Latin Music Database, which contains 3,227 music pieces categorized in 10 musical genres. The experimental results show that the employed features have different importance according to the part of the music signal from where the feature vectors were extracted. Furthermore, the ensemble approach provides better results than the individual segments in most cases

A Machine Learning Approach to Automatic Music Genre Classification

This paper presents a non-conventional approach for the automatic music genre classification problem. The proposed approach uses multiple feature vectors and a pattern recognition ensemble approach, according to space and time decomposition schemes. Despite being music genre classification a multi-class problem, we accomplish the task using a set of binary classifiers, whose results are merged in order to produce the final music genre label (space decomposition). Music segments are also decomposed according to time segments obtained from the beginning, middle and end parts of the original music signal (time-decomposition). The final classification is obtained from the set of individual results, according to a combination procedure. Classical machine learning algorithms such as Naïve-Bayes, Decision Trees, k Nearest-Neighbors, Support Vector Machines and Multi- Layer Perceptron Neural Nets are employed. Experiments were carried out on a novel dataset called Latin Music Database, which contains 3,160 music pieces categorized in 10 musical genres. Experimental results show that the proposed ensemble approach produces better results than the ones obtained from global and individual segment classifiers in most cases. Some experiments related to feature selection were also conducted, using the genetic algorithm paradigm. They show that the most important features for the classification task vary according to their origin in the music signal

The integrated data mining tool MineKit and a case study of its application on video shop data

The second goal of this paper is to report the result of evaluating MineKit in a real-world data set. This case study is relevant for data mining mainly for two reasons. First, the original data set, li ke a typical realworld data set, was not previously prepared for data mining activities, so that we had to spent a significant time preparing the data. Hence, we have actuall y gone through the most time-consuming phase of the knowledge discovery process. This issue is usually ignored in the data mining literature, which focus on the data mining phase only

Classificacao Automatica de Generos Musicais Utilizando Metodos de Bagging e Boosting

This paper presents a study that uses meta-learning techniques to the task of automatic music genre classification. The meta-learning techniques we used are Bagging and Boosting. In both cases the component classifiers used in both approaches are Decision Trees, k-NN (k nearest neighbors) and Naive Bayes. The experiments were performed on a dataset containing 1,000 songs with 10 different genres. The achieved results show that the Bagging approach is promising while the Boosting approach seems to be inadequate to the problem

The Latin Music Database: Uma Base de Dados Para a Classificacao Automatica de Generos Musicais

In this paper we present the Latin Music Database, a novel database of Latin musical recordings which was developed for automatic music genre classification but can also be used to other tasks related to music information retrieval (MIR) research. The method for assigning genres to the musical recordings is a novel one and it is based on human perception. Furthermore, the underlying framework allows that the database can be easily expanded and have all the features desired by the research community of audio information retrieval

A Trainable Algorithm for Summarizing News Stories

This work proposes a trainable system for summarizing news and obtaining an approximate argumentative structure of the source text. To achieve these goals we use several techniques and heuristics, such as detecting the main concepts in the text, connectivity between sentences, occurrence of proper nouns, anaphors, discourse markers and a binary-tree representation (due to the use of an agglomerative clustering algorithm). The proposed system was evaluated on a set of 800 documents