Characterizing chaotic melodies in automatic music composition

In this paper, we initially present an algorithm for automatic composition of melodies using chaotic dynamical systems. Afterward, we characterize chaotic music in a comprehensive way as comprising three perspectives: musical discrimination, dynamical influence on musical features, and musical perception. With respect to the first perspective, the coherence between generated chaotic melodies (continuous as well as discrete chaotic melodies) and a set of classical reference melodies is characterized by statistical descriptors and melodic measures. The significant differences among the three types of melodies are determined by discriminant analysis. Regarding the second perspective, the influence of dynamical features of chaotic attractors, e.g., Lyapunov exponent, Hurst coefficient, and correlation dimension, on melodic features is determined by canonical correlation analysis. The last perspective is related to perception of originality, complexity, and degree of melodiousness (Euler's gradus suavitatis) of chaotic and classical melodies by nonparametric statistical tests. (c) 2010 American Institute of Physics. [doi: 10.1063/1.3487516]

Music and faith: The religious scene in the Brazilian phonographic market

This text discusses the phonographic segment of religious music in Brazil in its two main manifestations, linked respectively to the Catholic and Protestant traditions. The text offers a brief history of both traditions, as well as a description of their main recording companies and artists of greatest prominence. In its final part. the text presents the strategies that bring together recording companies and independent artists, as well as ponders over Brazil`s independent musical production as a whole.

Predictability Analysis of Voice Signals Analyzing Healthy and Pathologic Samples

Research Foundation of the State of Sao Paulo (FAPESP)

Analyzing static three-dimensional elastic domains with a new infinite boundary element formulation

A new two-dimensionally mapped infinite boundary element (IBE) is presented. The formulation is based on a triangular boundary element (BE) with linear shape functions instead of the quadrilateral IBEs usually found in the literature. The infinite solids analyzed are assumed to be three-dimensional, linear-elastic and isotropic, and Kelvin fundamental solutions are employed. One advantage of the proposed formulation over quadratic or higher order elements is that no additional degrees of freedom are added to the original BE mesh by the presence of the IBEs. Thus, the IBEs allow the mesh to be reduced without compromising the accuracy of the result. Two examples are presented, in which the numerical results show good agreement with authors using quadrilateral IBEs and analytical solutions. (C) 2010 Elsevier Ltd. All rights reserved.

Exergy-based method for analyzing the composition of the electricity cost generated in gas-fired combined cycle plants

The proposed method to analyze the composition of the cost of electricity is based on the energy conversion processes and the destruction of the exergy through the several thermodynamic processes that comprise a combined cycle power plant. The method uses thermoeconomics to evaluate and allocate the cost of exergy throughout the processes, considering costs related to inputs and investment in equipment. Although the concept may be applied to any combined cycle or cogeneration plant, this work develops only the mathematical modeling for three-pressure heat recovery steam generator (HRSG) configurations and total condensation of the produced steam. It is possible to study any n x 1 plant configuration (n sets of gas turbine and HRSGs associated to one steam turbine generator and condenser) with the developed model, assuming that every train operates identically and in steady state. The presented model was conceived from a complex configuration of a real power plant, over which variations may be applied in order to adapt it to a defined configuration under study [Borelli SJS. Method for the analysis of the composition of electricity costs in combined cycle thermoelectric power plants. Master in Energy Dissertation, Interdisciplinary Program of Energy, Institute of Eletro-technical and Energy, University of Sao Paulo, Sao Paulo, Brazil, 2005 (in Portuguese)]. The variations and adaptations include, for instance, use of reheat, supplementary firing and partial load operation. It is also possible to undertake sensitivity analysis on geometrical equipment parameters. (C) 2007 Elsevier Ltd. All rights reserved.

The Effects of Live, Taped and No Music on People Experiencing Posttraumatic Amnesia

This study compared the effects of live, taped, and no music, on agitation and orientation levels of people experiencing posttraumatic amnesia (PTA). Participants (N = 22) were exposed to all 3 conditions, twice over 6 consecutive days. Songs used in the live and taped music conditions were identical and were selected based on participants' own preferred music. Pre and posttesting was conducted for each condition using the Agitated Behavior Scale (Corrigan, 1989) and the Westmead PTA Scale (Shores, Marosszeky, Sandanam, Batchelor, 1986). Participants' memory for the music used was also tested and compared with their memory for pictorial material presented in the Westmead PTA Scale. Results indicate that music significantly reduced agitation (p

The knowledge gap: A perspective on trade, industry, technology and policy from pre-war Australian music technology manufacturing (1903-30)

Australia struggles to achieve economic competitiveness, prevent expansion of the trade deficit and develop value-added production despite applications of policy strategies from protectionism to trade liberalisation. This article argues that these problems were emerging at the turn of the century, and that an investigation of music technology manufacturing in the first two decades of this century reveals fundamental problems in the conduct of relevant policy analysis. Analysis has focused on the trade or technology gap which is only symptomatic of an underlying knowledge gap. The article calls for a knowledge policy approach which can allow protection without the negative effects of isolation from global markets and without having to resort to unworkable utopian free-trade dogma. A shift of focus from a 'goods traded' view to a knowledge transaction (or diffusion) perspective is advocated.

Pursuing the popular in Australian history

No abstract

Time flies with music whatever its emotional valence

The present study used a temporal bisection task to investigate whether music affects time estimation differently from a matched auditory neutral stimulus, and whether the emotional valence of the musical stimuli (i.e., sad vs. happy music) modulates this effect. The results showed that, compared to sine wave control music, music presented in a major (happy) or a minor (sad) key shifted the bisection function toward the right, thus increasing the bisection point value (point of subjective equality). This indicates that the duration of a melody is judged shorter than that of a non-melodic control stimulus, thus confirming that ""time flies"" when we listen to music. Nevertheless, sensitivity to time was similar for all the auditory stimuli. Furthermore, the temporal bisection functions did not differ as a function of musical mode. (C) 2010 Elsevier B.V. All rights reserved.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bimanual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification. (C) 2009 Elsevier Inc. All rights reserved.