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

The Use of ART2 to create summaries from texts of different areas

The volume of documents available electronically is growing fast, so it becomes difficult to access and select desired information in a fast and efficient way. In this context the automatic summarization task assumes a very imperative role; therefore one seeks to reduce the size of a document, preserving to the maximum its informative content. In this paper, it’s applied a model which uses sentence clusters from an ART2 neural network to generate extractive summaries. Different models can be developed from distinct area documents. Hence, the aim of this work is to evaluate the performance of those models when they summarize documents from correlated or non correlated areas.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

kNNSumm: um sumarizador automático de documentos utilizando aprendizado baseado em instâncias

Neste trabalho é apresentada a arquitetura do kNNSumm (k-NN Summarizer), um sumarizador automático de documentos que utiliza o aprendizado de máquina baseado em instâncias. Também são apresentados os resultados obtidos com sua aplicação em uma coleção de documentos em inglês, extraídos da base TIPSTER, que é amplamente utilizada na literatura da área. Além disso, apresenta-se por meio de um exemplo simples e didático o funcionamento detalhado do sumarizador, e de uma forma geral também a tarefa de sumarização quando tratada por uma abordagem de aprendizado de máquina.In this work is presented the architecture of kNNSumm (k-NN Summarizer), an automatic document summarizer based on a instance based machine learning approach. The results achieved by its use on a document collection of english documents extracted from the TIPSTER base which is widely used in the literature are presented also. Additionally, we present a simple and didactic example of the procedures used by the summarizer, and in a more general way the text summarization task with machine learning.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

kNNSumm: um sumarizador automático de documentos utilizando aprendizado baseado em instâncias

Neste trabalho é apresentada a arquitetura do kNNSumm (k-NN Summarizer), um sumarizador automático de documentos que utiliza o aprendizado de máquina baseado em instâncias. Também são apresentados os resultados obtidos com sua aplicação em uma coleção de documentos em inglês, extraídos da base TIPSTER, que é amplamente utilizada na literatura da área. Além disso, apresenta-se por meio de um exemplo simples e didático o funcionamento detalhado do sumarizador, e de uma forma geral também a tarefa de sumarização quando tratada por uma abordagem de aprendizado de máquina.In this work is presented the architecture of kNNSumm (k-NN Summarizer), an automatic document summarizer based on a instance based machine learning approach. The results achieved by its use on a document collection of english documents extracted from the TIPSTER base which is widely used in the literature are presented also. Additionally, we present a simple and didactic example of the procedures used by the summarizer, and in a more general way the text summarization task with machine learning.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Ensemble of convolutional neural networks to improve animal audio classification

Abstract In this work, we present an ensemble for automated audio classification that fuses different types of features extracted from audio files. These features are evaluated, compared, and fused with the goal of producing better classification accuracy than other state-of-the-art approaches without ad hoc parameter optimization. We present an ensemble of classifiers that performs competitively on different types of animal audio datasets using the same set of classifiers and parameter settings. To produce this general-purpose ensemble, we ran a large number of experiments that fine-tuned pretrained convolutional neural networks (CNNs) for different audio classification tasks (bird, bat, and whale audio datasets). Six different CNNs were tested, compared, and combined. Moreover, a further CNN, trained from scratch, was tested and combined with the fine-tuned CNNs. To the best of our knowledge, this is the largest study on CNNs in animal audio classification. Our results show that several CNNs can be fine-tuned and fused for robust and generalizable audio classification. Finally, the ensemble of CNNs is combined with handcrafted texture descriptors obtained from spectrograms for further improvement of performance. The MATLAB code used in our experiments will be provided to other researchers for future comparisons at https://github.com/LorisNanni

The Use of ART2 to create summaries from texts of different areas

The volume of documents available electronically is growing fast, so it becomes difficult to access and select desired information in a fast and efficient way. In this context the automatic summarization task assumes a very imperative role; therefore one seeks to reduce the size of a document, preserving to the maximum its informative content. In this paper, it’s applied a model which uses sentence clusters from an ART2 neural network to generate extractive summaries. Different models can be developed from distinct area documents. Hence, the aim of this work is to evaluate the performance of those models when they summarize documents from correlated or non correlated areas.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI