Carlos Nascimento Silla

A survey of hierarchical classification across different application domains

In this survey we discuss the task of hierarchical classification. The literature about this field is scattered across very different application domains and for that reason research in one domain is often done unaware of methods developed in other domains. We define what is the task of hierarchical classification and discuss why some related tasks should not be considered hierarchical classification. We also present a new perspective about some existing hierarchical classification approaches, and based on that perspective we propose a new unifying framework to classify the existing approaches. We also present a review of empirical comparisons of the existing methods reported in the literature as well as a conceptual comparison of those methods at a high level of abstraction, discussing their advantages and disadvantages.

On the suitability of state-of-the-art music information retrieval methods for analyzing, categorizing and accessing non-Western and ethnic music collections

With increasing amounts of music being available in digital form, research in music information retrieval has turned into a dominant field to support organization of and easy access to large collections of music. Yet, most research is focussed traditionally on Western music, mostly in the form of mastered studio recordings. This leaves the question whether current music information retrieval approaches can also be applied to collections of non-Western and in particular ethnic music with completely different characteristics and requirements. In this work we analyze the performance of a range of automatic audio description algorithms on three music databases with distinct characteristics, specifically a Western music collection used previously in research benchmarks, a collection of Latin American music with roots in Latin American culture, but following Western tonality principles, as well as a collection of field recordings of ethnic African music. The study quantitatively shows the advantages and shortcomings of different feature representations extracted from music on the basis of classification tasks, and presents an approach to visualize, access and interact with ethnic music collections in a structured way.

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.

Novel top-down approaches for hierarchical classification and their application to automatic music genre classification

This paper presents two novel hierarchical classification methods which are extensions of a previously proposed selective classifier top-down approach, which consists of selecting — during the training phase — the best classifier at each node of a classifier tree. More precisely, we propose two novel selective top-down hierarchical methods. First, a method that selects the best feature set instead of the best classifier. Secondly, a method that selects both the best classifier and the best representation simultaneously. These methods are evaluated on the task of hierarchical music genre classification using four different types of feature sets extracted from each song and four classifiers.

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 (time decomposition). 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.

Automatic Bird Species Identification for Large Number of Species

In this paper we focus on the automatic identification of bird species from their audio recorded song. Bird monitoring is important to perform several tasks, such as to evaluate the quality of their living environment or to monitor dangerous situations to planes caused by birds near airports. We deal with the bird species identification problem using signal processing and machine learning techniques. First, features are extracted from the bird recorded songs using specific audio treatment, next the problem is performed according to a classical machine learning scenario, where a labeled database of previously known bird songs are employed to create a decision procedure that is used to predict the species of a new bird song. Experiments are conducted in a dataset of recorded songs of bird species which appear in a specific region. The experimental results compare the performance obtained in different situations, encompassing the complete audio signals, as recorded in the field, and short audio segments (pulses) obtained from the signals by a split procedure. The influence of the number of classes (bird species) in the identification accuracy is also evaluated.

Automatic Text Summarization with Genetic Algorithm-Based Attribute Selection

The task of automatic text summarization consists of generating a summary of the original text that allows the user to obtain the main pieces of information available in that text, but with a much shorter reading time. This is an increasingly important task in the current era of information overload, given the huge amount of text available in documents. In this paper the automatic text summarization is cast as a classification (supervised learning) problem, so that machine learning-oriented classification methods are used to produce summaries for documents based on a set of attributes describing those documents. The goal of the paper is to investigate the effectiveness of Genetic Algorithm (GA)-based attribute selection in improving the performance of classification algorithms solving the automatic text summarization task. Computational results are reported for experiments with a document base formed by news extracted from The Wall Street Journal of the TIPSTER collection–a collection that is often used as a benchmark in the text summarization literature.

A Global-Model Naive Bayes Approach to the Hierarchical Prediction of Protein Functions

In this paper we propose a new global--model approach for hierarchical classification, where a single global classification model is built by considering all the classes in the hierarchy -- rather than building a number of local classification models as it is more usual in hierarchical classification. The method is an extension of the flat classification algorithm naive Bayes. We present the extension made to the original algorithm as well as its evaluation on eight protein function hierarchical classification datasets. The achieved results are positive and show that the proposed global model is better than using a local model approach.

An Analysis of Sentence Boundary Detection Systems for English and Portuguese Documents

In this paper we present a study comparing the performance of different systems found in the literature that perform the task of automatic text segmentation in sentences for English documents. We also show the difficulties found to adapt these systems to make them work with Portuguese documents and the results obtained after the adaptation. We analyzed two systems that use a machine learning approach: MxTerminator and Satz, and a customized system based on fixed rules expressed by Regular Expressions. The results achieved by the Satz system were surprisingly positive for Portuguese documents.

Improving automatic music genre classification with hybrid content-based feature vectors

Current research on the task of automatic music genre classification has been focusing on new classification approaches based on combining information from other sources than the music signal. The reason for this is that the use of content-based approaches, i.e. using features extracted directly from the audio signal, seems to have reached a glass ceiling. In this work we show that by using different types of content-based features together it is possible to substantially improve the classification accuracy. This is an interesting result as different types of content-based features aim, at a conceptual level, to capture the same type of information. In order to identify which types of content-based features are responsible for the predictive accuracy gain, we also used a feature selection (FS) approach based on a genetic algorithm (GA). The analysis of the results in two databases shows that the use of the GA for FS succeeds in selecting a representative subset without significant loss in accuracy. It also shows that all the different types of content-based features employed are important for the improvement of the accuracy in classifying music genres.