Método LNEC para avaliação e classificação da qualidade acústica de edifícios habitacionais: ferramenta de estimação de custos médios de transição entre classes acústicas

O conforto acústico na habitação é essencial para permitir um repouso tranquilo e regenerativo. Este conforto é obtido principalmente pela redução do ruído e aumento do isolamento sonoro dos elementos de compartimentação. Para a qualificação deste conforto é necessário efetuar uma análise global do edifício, onde são considerados os fatores internos e externos à habitação, ou seja, considerando a acústica da envolvente (Vizinhança), do edifício (Edifício) e da fração (Habitação). Para este efeito foi produzido o “Método LNEC para avaliação e classificação da qualidade acústica de edifícios habitacionais”, o qual permite fazer uma avaliação global do conforto acústico na habitação. Este método inovador em Portugal origina uma Classe Acústica LNEC que permite representar com razoável fiabilidade o conforto acústico realmente sentido e a qualidade acústica da habitação. Este método pode ser aplicado a edifícios novos e a edifícios a reabilitar. De modo a permitir uma estimação dos custos médios necessários para transitar entre determinadas classes (e alcançar respetivo conforto acústico) é necessário possuir uma ferramenta de cálculo apropriada. Deste modo apresenta-se nesta comunicação uma ferramenta para estimar os custos de transição entre classes acústicas. Esta metodologia permite fazer escolhas mais fundamentadas nos processos de obtenção de determinado conforto acústico. Abstract The acoustic comfort in dwellings is essential to a peaceful and regenerative sleep. The achievement of this comfort is primarily obtained by reducing noise and increasing sound insulation of separating elements. To classify this comfort is necessary to conduct a comprehensive analysis of the habitation. This global analysis assesses the internal and external factors of the habitation, evaluating the acoustics of the environment (Vicinity), of the building (Building) and of the dwelling place (Lodging). Recently in Portugal was developed the “LNEC method for evaluation and acoustic quality classification of residential buildings” that allows an overall evaluation of the acoustic comfort in dwellings, resulting also in a “LNEC Acoustic Class” that portrays the real acoustic comfort sensed. Since this method can be applied to evaluate the acoustic comfort of new and of restored buildings, it is necessary a tool that gives an estimation of the needed investment to upgrade to a specific “LNEC Acoustic Class” (and achieve the respective acoustic comfort). In this communication is presented a tool that allows the estimation of that upgrade costs.

An integrative multidisciplinary approach to understanding cryptic divergence in Brazilian species of the Anastrepha fraterculus complex (Diptera: Tephritidae).

An integrative multidisciplinary approach was used to delimit boundaries among cryptic species within the Anastrepha fraterculus complex in Brazil. Sexual compatibility, courtship and sexual acoustic behaviour, female morphometric variability, variation for the mitochondrial gene COI, and the presence of Wolbachia were compared among A. fraterculus populations from the Southern (Vacaria, Pelotas, Bento Gonçalves, S~ao Joaquim) and Southeastern (Piracicaba) regions of Brazil. Our results suggest full mating compatibility among A. fraterculus populations from the Southern region and partial pre-zygotic reproductive isolation of these populations when compared with the population from the Southeastern region. A. fraterculus populations from both regions differed in the frequency of courtship displays and aspects of the calling phase and mounting acoustic signal. Morphometric analysis showed differences between Southern region and Southeastern region samples. All populations analyzed were infected with Wolbachia. The trees generated from the COI sequencing data are broadly congruent with the behavioural and morphometric data with the exception of one Southern population. The likely mechanisms by which A. fraterculus populations might have diverged are discussed in detail based on behavioural, morphometric, molecular genetics, and biogeographical studies

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

An automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference

In the last few years the number of systems and devices that use voice based interaction has grown significantly. For a continued use of these systems the interface must be reliable and pleasant in order to provide an optimal user experience. However there are currently very few studies that try to evaluate how good is a voice when the application is a speech based interface. In this paper we present a new automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference. Our study is based on a multi-language database composed by female voices. In the objective performance evaluation the system achieved a 7.3% error rate.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Control of thermal damage in grinding by digital signal processing of raw acoustic emission

Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.

Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Marine mammal species detection and classification

Thesis (Ph.D.)--University of Washington, 2016-04

Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.

Fast Transforms for Acoustic Imaging-Part I: Theory

The classical approach for acoustic imaging consists of beamforming, and produces the source distribution of interest convolved with the array point spread function. This convolution smears the image of interest, significantly reducing its effective resolution. Deconvolution methods have been proposed to enhance acoustic images and have produced significant improvements. Other proposals involve covariance fitting techniques, which avoid deconvolution altogether. However, in their traditional presentation, these enhanced reconstruction methods have very high computational costs, mostly because they have no means of efficiently transforming back and forth between a hypothetical image and the measured data. In this paper, we propose the Kronecker Array Transform ( KAT), a fast separable transform for array imaging applications. Under the assumption of a separable array, it enables the acceleration of imaging techniques by several orders of magnitude with respect to the fastest previously available methods, and enables the use of state-of-the-art regularized least-squares solvers. Using the KAT, one can reconstruct images with higher resolutions than was previously possible and use more accurate reconstruction techniques, opening new and exciting possibilities for acoustic imaging.

INVESTIGATION OF ACOUSTIC SIGNALS AS A TOOL FOR CHARACTERIZING SPOUTED BED DYNAMICS

The feasibility of characterizing the dynamics of a spouted bed based on acoustic emission (AE) signals is evaluated. Acoustic emission signals were measured in a semi-cylindrical Plexiglas column of diameter 150 mm and height 1000 mm with a conical base of internal angle 60 degrees and 25 mm inlet orifice diameter. Data were obtained for U/U(ms), from 0.3 to 2.0, static bed height from 250 to 500 mm, and glass beads of diameter 1.2 and 2.4 mm. AE signals reflected the effects of particle size and U/U(ms), but in general were insensitive to bed depth, even when there were drastic changes in spouting flow patterns. The results indicate that the AE signals were insensitive to the spouted bed hydrodynamics for the conditions studied. Overall, it appears that the AE analysis is unlikely to be a suitable technique for discriminating spouted bed flow regimes, at least for the range of frequencies and operating conditions investigated.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Acoustic analysis of speech through a hearing aid: perceptual effects of changes with two-channel compression

Compression amplification significantly alters the acoustic speech signal in comparison to linear amplification. The central hypothesis of the present study was that the compression settings of a two-channel aid that best preserved the acoustic properties of speech compared to linear amplification would yield the best perceptual results, and that the compression settings that most altered the acoustic properties of speech compared to linear would yield significantly poorer speech perception. On the basis of initial acoustic analysis of the test stimuli recorded through a hearing aid, two different compression amplification settings were chosen for the perceptual study. Participants were 74 adults with mild to moderate sensorineural hearing impairment. Overall, the speech perception results supported the hypothesis. A further aim of the study was to determine if variation in participants' speech perception with compression amplification (compared to linear amplification) could be explained by the individual characteristics of age, degree of loss, dynamic range, temporal resolution, and frequency selectivity; however, no significant relationships were found.