Improvement of accuracy in a sound synthesis method using Evolutionary Product Unit Networks

Auralization through binaural transfer path analysis and synthesis is a useful tool to analyze how contributions from different sources affect the perception of sound. This paper presents a novel model based on the auralization of sound sources through the study of the behavior of the system with respect to frequency. The proposed approach is a combined model using the airborne source quantification (ASQ) technique for low-mid frequencies (?2.5 kHz) and Evolutionary Product-Unit Neural Networks (EPUNNs) for high frequencies (>2.5 kHz), which improve overall accuracy. The accuracy of all models has been evaluated in terms of the Mean Squared Error (MSE) and the Standard Error of Prediction (SEP), the combined model obtaining the smallest value for high frequencies. Moreover, the best prediction model was established based on sound quality metrics, the proposed method showing better accuracy than the ASQ technique at high frequencies in terms of loudness, sharpness and 1/3rd octave bands.     

Sound quality analysis of cars using hybrid neural networks

In this paper, a procedure of testing and evaluation on the sound quality of cars are proposed and sound quality is analysed through the cars’ road running test on the providing ground, which was carried out with varying running speed. In addition to this experimental analysis, a neural network predictor is also designed to model the system for possible experimental applications. The proposed neural network is a recurrent type network, which consists of two types of neuron function in the hidden layer. As basic factors for sound quality, only objective factors are considered such as loudness, sharpness, speech intelligibility, and sound pressure level. The correlation between sound pressure level and another factor are discussed from a point of view of running speed dependency. Results of both computer simulations and experiments show that the neural predictor algorithm gives good results at accommodating different cases and provides superior prediction on two cars’ sound analysis.     

The experimental investigation of the effects of uncoated,PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks

In this study the machining of AISI 1030 steel (i.e. orthogonal cutting) uncoated, PVD- and CVD-coated cemented carbide insert with different feed rates of 0.25, 0.30, 0.35, 0.40 and 0.45 mm/rev with the cutting speeds of 100, 200 and 300 m/min by keeping depth of cuts constant (i.e. 2 mm), without using cooling liquids has been accomplished. The surface roughness effects of coating method, coating material, cutting speed and feed rate on the workpiece have been investigated. Among the cutting tools—with 200 mm/min cutting speed and 0.25 mm/rev feed rate—the TiN coated with PVD method has provided 2.16 μm, TiAlN coated with PVD method has provided 2.3 μm, AlTiN coated with PVD method has provided 2.46 μm surface roughness values, respectively. While the uncoated cutting tool with the cutting speed of 100 m/min and 0.25 mm/rev feed rate has yielded the surface roughness value of 2.45 μm. Afterwards, these experimental studies were executed on artificial neural networks (ANN). The training and test data of the ANNs have been prepared using experimental patterns for the surface roughness. In the input layer of the ANNs, the coating tools, feed rate (f) and cutting speed (V) values are used while at the output layer the surface roughness values are used. They are used to train and test multilayered, hierarchically connected and directed networks with varying numbers of the hidden layers using back-propagation scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) algorithms with the logistic sigmoid transfer function. The experimental values and ANN predictions are compared by statistical error analyzing methods. It is shown that the SCG model with nine neurons in the hidden layer has produced absolute fraction of variance (R²) values about 0.99985 for the training data, and 0.99983 for the test data; root mean square error (RMSE) values are smaller than 0.00265; and mean error percentage (MEP) are about 1.13458 and 1.88698 for the training and test data, respectively. Therefore, the surface roughness value has been determined by the ANN with an acceptable accuracy.     

数字助听器中新的非线性响度补偿方法的研究

数字助听器中的多通道响度补偿算法,通常是在划分出的多个频段内分别进行补偿,这在一定程度上会导致共振峰结构的破坏,造成语义信息的曲解或丢失。一种新的非线性的响度补偿方法的提出,无需进行频段的划分,并且根据听损患者在不同频率上对声音强弱的实时感知特性对语音信号进行补偿。该方法不仅使补偿后语音的响度完全映射到听者的听觉范围内,还使得补偿后的响度曲线在轮廓上与原始语音基本保持一致。实验中,小波去噪和移频技术的运用保证了处理效果更加优良。结果表明,该方法能有效提高语音响度和改善听损患者的言语识别率,具有一定的应用价值。     

Personal noise exposures of operators of agricultural tractors

Approximately one million agricultural tractors are used in Turkey for crop production and about one-third of the population lives in rural areas. The objectives of this study were to determine sound pressure levels, A-weighted sound pressure levels, and the permissible exposure time for tractors without cabins, field-installed cabins, and original cabins at ear level of agricultural tractor operators for following machines: plows, cultivators, top soil cultivators, rotary tillers, tool combinations (harrow + roller), mechanical drills, pneumatic drills, chemical applicators, fertilizer applicators, drum mowers, balers, and forage harvesters.Variance analyses showed that type of operation, type of cabins, and operation × cabin interactions were statistically significant (P < 0.01) both for sound pressure levels and equivalent (A-weighted) sound pressure levels. The use of original cabins had a greater effect in decreasing average sound pressures and resulted in more efficient noise insulation, especially at higher center frequencies compared to field-installed cabins whereas field-installed cabins proved to be more favorable compared to tractors without cabins. Sound pressure levels at 4000 Hz center frequency was reduced 2-13 dB and 4-18 dB by using a field-installed cabin and an original cabin, respectively. The measured A-weighted equivalent sound pressure levels were compared to the threshold limit level, and was concluded that depending on the cabin types used, the operators could usually work from 4 to 6 h a day without suffering from noise induced inconveniences while 2-3 h is permissible for plowing and forage harvesting on tractors without cabins. Due to timeliness considerations in agricultural machine operations, a farmer would not be willing to interrupt the operation based on permissible exposure time set by the standards.Based on the findings of this study, particularly an original cabin is recommended to reduce machine-induced noise below the danger limit during agricultural machine operations. Personal protection devices should be used when tractors are operated without cabins, which could reduce A-weighted equivalent sound pressure levels by 10-45 dB(A).     

像素级图像优化融合客观评价指标分析

文章对像素级图像融合的客观评价指标进行分析,设计了新的交互信息量指标,避免了信息的重载,并根据指标相关度和指标选取原则对图像融合的指标进行评价和选取,形成了一组有效的评价准则,使图像融合质量评价指标较为完备,实验验证了方法的有效性。     

Influence of image‐quality degradation on accommodation mechanism in human vision: Conditions necessary for objective image‐quality evaluation metrics

This paper clarifies the relationship between image quality and accommodation in human vision through two types of experiments. One examines how image‐quality degradation influences the accommodation mechanism, and the other examines which type of information of quality‐degraded images activates the accommodation mechanism. Actually, accommodative responses are measured using an infrared optometer while subjects are subjectively evaluating sharpness, noise, and pseudo‐contours and while they are observing sine, square, and missing fundamental (MF) square waves. The following results were derived: (1) the accommodation lag increases as the degree of sharpness is degraded regardless of the tone‐reproducing methods; (2) the accommodation lag decreases considerably in the existence of noise or pseudo‐contours, whereas it increases for uniform or gently curved planes; (3) the spatial features of presented images activate the accommodation mechanism. These results suggest that accommodative responses influence human subjective judgments as well as being a human factor related closely to image quality and that the spatial features of quality‐degraded images underlie human subjective judgments. In other words, they imply that objective image‐quality evaluation metrics should satisfy the following two conditions: the incorporation of the accommodation characteristics into such metrics and the formulation of such metrics in the spatial region.     

Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease

The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using forced oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n = 25; healthy, n = 25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se > 87%, Sp > 94%, and AUC > 0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using forced oscillation measurements.     

Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)

In this paper the possibility of predicting salt concentrations in soils from measured reflectance spectra is studied using partial least squares regression (PLSR) and artificial neural network (ANN). Performance of these two adaptive methods has been compared in order to examine linear and non-linear relationship between soil reflectance and salt concentration.Experiment-, field- and image-scale data sets were prepared consisting of soil EC measurements (dependent variable) and their corresponding reflectance spectra (independent variables). For each data set, PLSR and ANN predictive models of soil salinity were developed based on soil reflectance data. The predictive accuracies of PLSR and ANN models were assessed against independent validation data sets not included in the calibration or training phase.The results of PLSR analyses suggest that an accurate to good prediction of EC can be made based on models developed from experiment-scale data (R² > 0.81 and RPD (ratio of prediction to deviation) > 2.1) for soil samples salinized by bischofite and epsomite minerals. For field-scale data sets, the PLSR predictive models provided approximate quantitative EC estimations (R² = 0.8 and RPD = 2.2) for grids 1 and 6 and poor estimations for grids 2, 3, 4 and 5. The salinity predictions from image-scale data sets by PLSR models were very reliable to good (R² between 0.86 and 0.94 and RPD values between 2.6 and 4.1) except for sub-image 2 (R² = 0.61 and RPD = 1.2).The ANN models from experiment-scale data set revealed similar network performances for training, validation and test data sets indicating a good network generalization for samples salinized by bischofite and epsomite minerals. The RPD and the R² between reference measurements and ANN outputs of theses models suggest an accurate to good prediction of soil salinity (R² > 0.92 and RPD > 2.3). For the field-scale data set, prediction accuracy is relatively poor (0.69 > R² > 0.42). The ANN predictive models estimating soil salinity from image-scale data sets indicate a good prediction (R² > 0.86 and RPD > 2.5) except for sub-image 2 (R² = 0.6 and RPD = 1.2).The results of this study show that both methods have a great potential for estimating and mapping soil salinity. Performance indexes from both methods suggest large similarity between the two approaches with PLSR advantages. This indicates that the relation between soil salinity and soil reflectance can be approximated by a linear function.     

Satellite passive microwave remote sensing for monitoring global land surface phenology

Vegetation phenology characterizes seasonal life-cycle events that influence the carbon cycle and land-atmosphere water and energy exchange. We analyzed global phenology cycles over a six year record (2003-2008) using satellite passive microwave remote sensing based Vegetation Optical Depth (VOD) retrievals derived from daily time series brightness temperature (T_b) measurements from the Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and other ancillary data inputs. The VOD parameter derives vegetation canopy attenuation at a given microwave frequency (18.7 GHz) and varies with canopy height, density, structure and water content. An error sensitivity analysis indicates that the retrieval algorithm can resolve the VOD seasonal cycle over a majority of global vegetated land areas. The VOD results corresponded favorably (p < 0.01) with vegetation indices (VIs) and leaf area index (LAI) information from satellite optical-infrared (MODIS) remote sensing, and phenology cycles determined from a simple bioclimatic growing season index (GSI) for over 82% of the global domain. Lower biomass land cover classes (e.g. savannas) show the highest correlations (R = 0.66), with reduced correspondence at higher biomass levels (0.03 < R < 0.51) and higher correlations for homogeneous land cover areas (0.41 < R < 0.83). The VOD results display a unique end-of-season signal relative to VI and LAI series, and may reflect microwave sensitivity to the timing of vegetation biomass depletion (e.g. leaf abscission) and associated changes in canopy water content (e.g. dormancy preparation). The VOD parameter is independent of and synergistic with optical-infrared remote sensing based vegetation metrics, and contributes to a more comprehensive view of land surface phenology.     

A pseudo-waveform technique to assess forest structure using discrete lidar data

The use of airborne laser scanning systems (lidar) to describe forest structure has increased dramatically since height profiling experiments nearly 30 years ago. The analyses in most studies employ a suite of frequency-based metrics calculated from the lidar height data, which are systematically eliminated from a full model using stepwise multiple linear regression. The resulting models often include highly correlated predictors with little physical justification for model formulation. We propose a method to aggregate discrete lidar height and intensity measurements into larger footprints to create “pseudo-waves”. Specifically, the returns are first sorted into height bins, sliced into narrow discrete elements, and finally smoothed using a spline function. The resulting “pseudo-waves” have many of the same characteristics of traditional waveform lidar data. We compared our method to a traditional frequency-based method to estimate tree height, canopy structure, stem density, and stand biomass in coniferous and deciduous stands in northern Wisconsin (USA). We found that the pseudo-wave approach had strong correlations for nearly all tree measurements including height (cross validated adjusted R² (R²_cv) = 0.82, RMSE_cv = 2.09 m), mean stem diameter (R²_cv = 0.64, RMSE_cv = 6.15 cm), total aboveground biomass (R²_cv = 0.74, RMSE_cv = 74.03 kg ha^− 1), and canopy coverage (R²_cv = 0.79, RMSE_cv = 5%). Moreover, the type of wave (derived from height and intensity or from height alone) had little effect on model formulation and fit. When wave-based and frequency-based models were compared, fit and mean square error were comparable, leading us to conclude that the pseudo-wave approach is a viable alternative because it has 1) an increased breadth of available metrics; 2) the potential to establish new meaningful metrics that capture unique patterns within the waves; 3) the ability to explain metric selection based on the physical structure of forests; and 4) lower correlation among independent variables.     

Preliminary study on the accuracy of respiratory input impedance measurement using the interrupter technique

Respiratory input impedance contains information about the state of pulmonary mechanics in the frequency domain. In this paper the possibility of respiratory impedance measurement by interrupter technique as well as the accuracy of this approach are assessed. Transient states of flow and pressure recorded during expiratory flow interruption are simulated with a complex, linear model for the respiratory system and then used to calculate the impedance, including three states of respiratory mechanics and the influence of the measurement noise. The results of computations are compared to the known, theoretical impedance of the model. At 1 kHz sampling rate, the optimal time window lays between 100 and 200 ms and is centred around the pressure jump caused by the flow interruption. The proposed algorithm yields satisfactory accuracy in the range from 10 to 400 Hz, particularly to 150 Hz. Depending on the simulated respiratory system state, the error of calculated impedance (relative Euclidean distance between the vectors of computed and theoretical values), for the window of 190 ms, varies between 5.0% and 7.1%.     

变压器可听声信号特征分析

基于振动与可听声的变压器诊断技术凭借其与被监测设备无电气连接、抗干扰能力强和灵敏度高等优点成为研究热点。本文为分析变压器可听声信号特征,理论分析了变压器可听声产生机理及其影响因素。运用分层阈值消噪法对信号进行消噪,采用快速傅里叶变换和小波分解法对不同运行年限、不同电压等级、不同采集位置、不同负载率情况下的在运变压器可听声信号进行分析,得到信号的频谱和小波能量特征向量。结果表明,理论上变压器可听声信号主频为100Hz,并存在200Hz、300 Hz等高次谐波分量;但是,随着变压器运行年限、电压等级的升高,主频增大为200Hz,高次谐波占比及高频段能量特征值增大;信号频谱与能量特征值受采集位置影响,检测时应根据变压器结构等实际情况选取合适的采集位置。     

Nature of sensitive high-order resonant modes in piezoelectric excited millimeter sized cantilever (PEMC) sensors

High-order resonant modes of piezoelectric-excited cantilever (PEMC) sensors were previously shown to be very highly sensitive at 0.3-2 fg/Hz for in-liquid applications. The purpose of this work is to show experimentally and with finite element model (FEM) simulations that such sensitive modes are strongly influenced by the sensor width, suggesting that the sensitive modes are torsional or buckling modes. From experimental observations the resonant frequency of high-order modes had a strong dependence on width, where a sensor with a smaller width had resonant modes at a higher frequency. Also the FEM simulations indicate that in this frequency range there are resonant modes with a buckling nature that change for a decrease in width, consistent with experimental observations. In order to establish that the width-dependent modes are mass-sensitive in liquids, resonant frequency change to density changes in flow experiments under fully liquid-immersed conditions were determined. Average frequency shifts of 475 ± 49 Hz (n = 5), 533 ± 31 Hz (n = 5), 715 ± 103 Hz (n = 5) and 725 ± 37 Hz (n = 5) were obtained for the four designs investigated in response to a density change of 0.0118 g/cm³. The results show that the resonant frequency response to variations in the geometry provides insightful data on the role of width in PEMC sensor design.     

Copper oxide nanoparticle sensors for hydrogen cyanide detection: Unprecedented selectivity and sensitivity

In the current work, CuO nanoparticles were synthesized in a facile way, and characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and thermogravimetry. Using these CuO nanoparticles, CuO functionalized QCM resonators were fabricated and explored for HCN sensing. The sensing properties were studied by a sensor characterization system coupled with a mass spectrometer. The sensor response to HCN (+347 Hz) was found to be in an opposite direction as compared with other common volatile substances (ether: −1230 Hz; water: −1815 Hz; n-hexane: −2100 Hz; benzene: −3410 Hz; acetic acid: −4840 Hz; ethanol: −6270 Hz), offering excellent selectivity for HCN detection. In addition, the sensitivity (15.1 Hz/μg) was very high, and the response (30 s) and recovery (750 s) were very fast. A sensing mechanism was proposed based on experimental results, in which a surface redox reaction occurs between CuO and Cu₂O on the nanoparticle reversibly upon contact with HCN and air, respectively. The current results would provide an exciting alternative to fast, sensitive and selective detection of trace HCN, which would be of particular benefit in the area of public security and environmental applications.     

Training-free referenceless camera image blur assessment via hypercomplex singular value decomposition

Blur plays an important role in the perception of camera image quality. Generally, blur leads to attenuation of high frequency information and accordingly changes the image energy. Quaternion describes the color information as a whole. Recent researches in quaternion singular value decomposition show that the singular values and singular vectors of the quaternion can capture the distortion of color images, and thus we reasonably suppose that singular values can be utilized to evaluate the sharpness of camera images. Motivated by this, a novel training-free blind quality assessment method considering the integral color information and singular values of the distorted image is proposed to evaluate the sharpness of camera images. The blurred camera image is first converted to LAB color space and divided into blocks. Then pure quaternion is utilized to represent pixels of the blurred camera image and the energy of every block are obtained. Inspired by the human visual system appears to assess image sharpness based on the sharpest region of the image, the local sharpness normalized energy is defined as the sharpness score of the blurred camera image. Experimental results have demonstrated the effectiveness of the proposed metric compared with popular sharpness image quality metrics.

     

The perceived quality of in-vehicle auditory signals: a structural equation modelling approach

Abstract

The current study applied Structural Equation Modelling to analyse the relationship among pitch, loudness, tempo and timbre and their relationship with perceived sound quality. Twenty-eight auditory signals of horn, indicator, door open warning and parking sensor were collected from 11 car brands. Twenty-one experienced drivers were recruited to evaluate all sound signals with 11 semantic differential scales. The results indicate that for the continuous sounds, pitch, loudness and timbre each had a direct impact on the perceived quality. Besides the direct impacts, pitch also had an impact on loudness perception. For the intermittent sounds, tempo and timbre each had a direct impact on the perceived quality. These results can help to identify the psychoacoustic attributes affecting the consumers’ quality perception and help to design preferable sounds for vehicles. In the end, a design guideline is proposed for the development of auditory signals that adopts the current study’s research findings as well as those of other relevant research.

Practitioner Summary: This study applied Structural Equation Modelling to analyse the relationship among pitch, loudness, tempo and timbre and their relationship with perceived sound quality. The result can help to identify psychoacoustic attributes affecting the consumers’ quality perception and help to design preferable sounds for vehicles.     

心理声学参数提取及其在目标识别中的应用

根据人耳听觉特性,研究了心理声学中人耳识别目标的重要特征参数在目标识别中的应用.针对无线电噪声和舰船辐射噪声,利用Zwicker理论提取心理声学参数中的特性响度和特性尖锐度作为识别特征,通过神经网络分类器分别对这两组噪声各三类进行分类识别研究.实验表明特性响度和特性尖锐度主要反映了目标的振幅特性,可以正确识别目标并具有较高的识别率.是有效的识别特征.由于特性响度和特性尖锐度反映目标的特性相同,利用遗传算法仅对特性响度特征进行优化选择.挑选出特性响度中的分类关键量,降低识别空间的维数,提高识别率.     

Fractional order model parameters for the respiratory input impedance in healthy and in asthmatic children

This paper provides an evaluation of a fractional order model for the respiratory input impedance, using two groups of subjects, respectively healthy and asthmatic children. The purpose is to verify if the model is able to deliver statistically meaningful parameter values in order to classify the two groups. The data are gathered with the non-invasive lung function test of forced oscillations technique, by means of a multisine signal within the 4-48 Hz frequency range. Based on our previous work, a fractional order model for this range of frequencies is obtained. Additional parameters are proposed to evaluate the two groups. The results indicate that the model was unable to detect significant changes between the asthmatic children with normal spirometry results (as result of medication) and the healthy children. Due to medication intake during the hours prior to the exam, bronchial challenge did not modify substantially the respiratory parameters. Our findings correspond to similar studies reported in the specialized literature. Combined model parameters, such as the tissue damping and the tissue elastance were significantly different in the two groups (p < 0.01). Two extra indexes are introduced: the quality factor and the power factor, providing significantly different results between the two groups (p ? 0.01). We conclude that the model can be used in the respective frequency range to characterize the two groups efficiently.     

A comparative study of hardware architectures for lightweight block ciphers

A hardware-based performance comparison of lightweight block ciphers is conducted in this paper. The DESL, DESXL, CURUPIRA-1, CURUPIRA-2, HIGHT, PUFFIN, PRESENT and XTEA block ciphers have been employed in this comparison. Our objective is to survey what ciphers are suitable for security in Radio Frequency Identification (RFID) and other security applications with demanding area restrictions. A general architecture option has been followed for the implementation of all ciphers. Specifically, a loop architecture has been used, where one basic round is used iteratively. The basic performance metrics are the area, power consumption and hardware resource cost associated with the implementation resulting throughput of each cipher. The most compact cipher is the 80-bit PRESENT block cipher with a count of 1704 GEs and 206.4 Kbps, while the largest in area cipher is the CURUPIRA-1. The CURUPIRA-1 cipher consumes the highest power of 118.1 μW, while the PRESENT cipher consumes the lowest power of 20 μW. All measurements have been taken at a 100 kHz clock frequency.