Acoustic inter- and intra-room similarity based on room acoustic parameters

This paper shows various approaches for determining acoustic (dis-)similarity based on room acoustic parameter values derived from real measurements. The similarity is calculated across different room configurations and/or between different microphone-loudspeaker positions within the same room configuration. We compare supervised (LDA, Random Forrest) and unsupervised techniques (PCA, SPPA) and pre-selected visualizations in terms of their ability to exhibit inter- and intra-room (dis-)similarities. The data set generated comprises spatially high-resolution room impulse responses obtained from multiple source-receiver positions within a room configuration. The room acoustics are varied by introducing active walls and geometries accounting for specific room configurations. The results show that the separation of room configurations primarily relies on specific acoustic parameters, with the reverberation time playing an important role. Within a given room configuration, the acoustic parameters excluding the reverberation time mainly capture the orientation and distance between the source and receiver

Contributions to projection pursuit learning

Orientadores: Fernando Jose Von Zuben, Clodoaldo Aparecido de Moraes LimaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: A obtenção de modelos parcimoniosos é uma necessidade em vários problemas de engenharia, como no caso de projeto de sistemas embarcados. Algoritmos construtivos para treinamento supervisionado têm apresentado efetividade como metodologias de projeto de redes neurais artificiais (RNAs) parcimoniosas, de boa acurácia e capacidade de generalização, embora requeiram mais recursos computacionais durante o processo de síntese da RNA. O aprendizado por busca de projeção está entre os métodos construtivos mais utilizados, mas ainda apresenta algumas limitações, sendo que aqui se procura tratar três delas: a inicialização da direção de projeção, o emprego de entrada de polarização junto aos neurônios da camada intermediária e a seleção de variáveis visando a redução no número de entradas, ou seja, na dimensão do vetor de projeção. Utilizou-se uma técnica de seleção de variáveis denominada wrapper, cuja implementação envolveu o emprego de um algoritmo genético, e realizaram-se experimentos de análise de desempenho no contexto de predição de séries temporais, indicando que as três propostas sugeridas trazem contribuições para o processo de aprendizado construtivoAbstract: The production of parsimonious models is a common demand on a wide variety of engineering problems, as in the design of embedded systems. Constructive algorithms for supervised learning have shown to be effective methodologies for the synthesis of parsimonious artificial neural networks, with high levels of accuracy and generalization capability, though requiring more computational resources during the training phase. Even being one of the most frequently adopted constructive learning methods, the projection pursuit learning algorithm still presents some limitations, and three of them will be treated here: the initialization of the direction of projection, the use of a bias term at the input of the hidden-layer neurons, and the selection of input variables as a form of reducing the number of inputs, i.e. the dimension of the vector of projection. The variable selection technique adopted here is denoted wrapper, and a genetic algorithm was considered as the search engine. The performance analysis has been carried out by experiments involving time series prediction, indicating that the three propositions suggested to deal with limitation of projection pursuit learning contribute favorably to the process of constructive learningMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Visual and semantic interpretability of projections of high dimensional data for classification tasks

A number of visual quality measures have been introduced in visual analytics literature in order to automatically select the best views of high dimensional data from a large number of candidate data projections. These methods generally concentrate on the interpretability of the visualization and pay little attention to the interpretability of the projection axes. In this paper, we argue that interpretability of the visualizations and the feature transformation functions are both crucial for visual exploration of high dimensional labeled data. We present a two-part user study to examine these two related but orthogonal aspects of interpretability. We first study how humans judge the quality of 2D scatterplots of various datasets with varying number of classes and provide comparisons with ten automated measures, including a number of visual quality measures and related measures from various machine learning fields. We then investigate how the user perception on interpretability of mathematical expressions relate to various automated measures of complexity that can be used to characterize data projection functions. We conclude with a discussion of how automated measures of visual and semantic interpretability of data projections can be used together for exploratory analysis in classification tasks.Comment: Longer version of the VAST 2011 poster. http://dx.doi.org/10.1109/VAST.2011.610247

A survey of feature selection methods : algorithms and software

textThe feature selection problem is a major component in disease surveillance since data sources are so costly. This report describes several existing methods for performing feature selection along with software that implements these methods. To help make experimenting with different algorithms easy, we have created a feature selection wrapper package in Python. This wrapper allows the user to easily try different algorithms on the same data set and visualize the results. Experiments are performed to validate that the methods perform as expected.Operations Research and Industrial Engineerin

Dimension Reduction in Big Data Environment-A Survey

Relational database management system is able to tackle data set which is structured in some way and by means of querying to the system user gets certain answer. But if the data set itself does not lie under any sort of structure, it is generally very tedious job for user to get answer to certain query. This is the new challenge coming out for the last decade to the scientists, researchers, industrialists and this new form of data is termed as big data. Parallel computation not only from the concept of hardware, but different application dependent software is now being developed to tackle this new data set for solving the challenges generally attached with large data set such as data curation, search, querying, storage etc. Information sensing devices, RFID readers, cloud storage now days are making data set to grow in an increasing manner. The goal of big data analytics is to help industry and organizations to take intelligent decisions by analyzing huge number of transactions that remain untouched till today by conventional business intelligent systems. As the size of dataset grows large also with redundancy, software and people need to analyze only useful information for particular application and this newly reduced dataset are useful compare to noisy and large data