|Digital Signal Processing||
Listening Enhancement in Noisy Environment Based on Spectral Decomposition and Adaptive Dynamic Range Compression of the Signal
A general person often experiences problems when listening to audiobooks or music in noisy environment. Audio signal becomes partially or fully imperceptible due to the masking effect. Just increasing playback volume in noisy conditions is excessively tiresome for ears because masked sounds become loud and unmasked sounds become too loud. On the other hand manual adjustment of playback volume is inconvenient especially when noise intensity gradually changes.
The algorithm proposed in the paper is based on frequency decomposition and adaptive dynamic compression of the signal. Dynamic range of each subband is narrowed in order to make it perceivable after mixing with background noise. Response curve of the compressor is adaptively adjusted according to short-time energy of the noise.
We designed a smartphone application that implements the proposed method. The application captures background noise using the microphone and performs playback of the sounds through near end listening enhancement scheme. The subjective listening tests show that algorithm significantly improves listening experience in different noisy conditions including traffic and engine noises. Some objective evaluations have been done as well using sound pressure levels (SPL) and SII measurements.
Keywords: wavelet decomposition, neuron networks, variable structure, cosmic rays variation, anomalous features.
Observations of CR intensity variations are used in a number of fundamental and applied research related to monitoring and forecast of the space weather. CR variations observed on the surface of the Earth are the result of various integral solar, heliospheric and atmospheric phenomena and have a complex internal structure. During periods of strong increases in the intensity (GLE-events) extensive flows of high-energy particles create a major problem for space equipment and other technologies, for wireless communication in the polar regions, as well as for astronauts. Therefore, allocation and prediction of such events is very important.
In this paper, based on the proposed method, we researched data of neutron monitor from stations "Cape Schmidt" (Russia, Cape Schmidt) and "Apatite" (Russia, Apatity). Trained neural networks, which approximate CR variations for the analyzed stations, showed that in periods of quiet geomagnetic field, which are characterized by low solar activity, the absolute values of errors does not exceed the value of 0.05, indicating a good quality of approximating properties. In periods of high geomagnetic activity, there was a significant increase in network errors caused by changes in the state of near-Earth space and the decrease in the level of cosmic rays (Forbush decrease). Increase in errors of networks was also observed before and after the analyzed GLE-event. These results confirm the effectiveness of the proposed method and the possibility of its use in the problems of detailed analysis of cosmic ray variations and detection of anomalies that occur during periods of increased solar activity.
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Developed methodology can be applied to characteristics forecast with gridded regularly obtained satellite and/or surface measured data as input. Each node of planar regular (uniform) grid defined in rectangular spatial area provides time series of characteristic values. All the time series have the same start time point, time span and length (number of sequential time points). The task is to offer extrapolation to each time series using the information about variability of not only one time series. Forecast horizon is significantly less than available time series fragment length (about several units and several hundreds time points respectively). Our solution is based on multivariate time series analysis, clustering techniques and linear algebra numerical methods. It consists of three stages: grid nodes clustering, initial intracluster forecast and adjusted intracluster forecast.
Clustering uses cross-correlation and spatial neighbouring criterion to divide nodes without metric given directly. Algorithm contains divisive and agglomerative steps. At the first step grid division by four equal rectangles is carried out and continued similarly for each obtained rectangle until any pair of current cluster nodes has corresponding time series cross-correlation coefficient greater than predetermined value and is greater than the same coefficient calculated for this pair time series fragments at nonzero time lag. As the result, set of grid nodes clusters and set of corresponding multivariate cluster series are formed. Finally, cluster characteristics are proposed to estimate cluster structure stability and consistency.
Initial intracluster forecast adopts three modern time series handling techniques such as multivariate empirical mode decomposition (MEMD) and quasi-stationarity condition application to determine length of time series fragment to be used further as two preprocessing tools. Forecast is calculated with so-called K-extension method based on multivariate singular spectrum analysis (MSSA). At this stage cluster series extrapolation is carried out for each cluster separately and it uses only one (current) cluster series dataset. Then series of forecast errors (one-step ahead, horizon is equal to 1) is computed to be used at the next stage.
MSSA forecast results correction (adjusted intracluster forecasting method) is based on univariate time series approximation with threshold autoregressive model with external inputs (TARX). MSSA forecasting method uses special autoregressive model to approximate multivariate time series corresponding to particular cluster. Since the aim here is to employ some spatio-temporal effects and dependancies between different clusters, it appears more relevant to account for some explanatory variables in addition to simple autoregressive model. Therefore, TARX is used for corrected cluster (each cluster in turns) one-step ahead forecast error prediction with regressors being previously calculated errors for both this cluster and some set of the correcting clusters determined with cluster characteristics values analysis.
Developed methodology was applied successfully for sea surface anomalies in the Barents Sea and for sea surface temperature in the Irminger Sea.
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At present the most accurate standalone solution of aircraft navigation problem is achieved by map-matching radionavigation systems, especially by systems built on the base of two-dimensional topography maps usage. Comparison of prepared reference map to created during flight measured map performed in such systems by calculating so called proximity index. Global extremum of proximity index defines deviation between real aircraft coordinates and expected coordinates.
Algorithms of correlational-extremal processing differ in the choice of proximity index type. Type of proximity index specifies calculation complexity of particular algorithm. For classic correlational algorithm (CCA) proximity index is represented by mutual correlation function of reference and measured maps. For the family of differential algorithms multiplication is replaced by subtraction, allowing to significantly reduce requirements of calculation unit performance. This paper deals with analysis of the following differential algorithms: algorithm of absolute difference minimum (ADM), algorithm of squared difference minimum with average subtracting (SSM), algorithm of absolute difference minimum with average subtracting (ASM).
Measured maps obtained by processing of information extracted during the area relief scanning is commonly used in map-matching navigation systems. Relief scanning performed by onboard multiray radar. Radar rays are stationary relative to aircraft. Relief scanning is realized due to aircraft motion. Different characteristics of algorithms were compared using mathematical modeling and showed ADM algorithm to provide the worst coordinate estimation reliability. Three other algorithms calculations contain operation of centering which allows to compensate systematic error and to increase the reliability of coordinate estimation. CCA provides worst calculation time, which caused by large amount of multiplication operations. SSM algorithm is preferred with respect to higher coordinate estimation reliability and lower calculation time requirement.
Calculation time required to perform calculation according to SSM algorithm allows to realize this algorithm using modern hardware. In particular, recently developed integrated module with high-performance signal processors can be used for algorithm implementation.
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One condition check algorithm of the ratio ensures the maximum relative error δìî=4,08% for 6 operations N+P: comparison, multiplication, addition and constant extraction. It should be noted that only the computation within the interval with the error of 4.6% of the best approximation polynomial requires the implementation of 10 operations: 6 N and 4 P. When using two conditions of ratio comparison the polynomials approximating the function with the error δìî of less than 1.4% are obtained. When implementing the algorithm it should be performed 9 N+P operations and stored 5 constants in the memory. Compared with the one condition algorithm when increasing the number of operations by 3 the error δìî decreases by 2.9 times. When applying three conditions the error δìî decreases to 0.5% that is by 2.8 times less than that for two condition algorithm. Number of N+P operations on the longest branch of the algorithm implementation corresponds to 12, and there should be stored 8 constants in the memory. For four condition algorithm the error does not exceed 0.26% with approximate reproduction of functions for 15 operations. Upon this, 11 constants should be stored in the memory. When increasing the number of operations N+P by 3 the error decreased almost by 2 times. The further increase in the number of conditions complicates the algorithm implementation and for higher accuracy class systems is preferable to use the direct methods of square root computation.
The division operation A/B is substituted by multiplication of the numerator by the reciprocal of the denominator: A/B = A x (1 / B). The function is approximated in the range by three polynomials of best approximation on three subintervals with approximately equal absolute maximum errors. The phase measurement using the predetermined approximation subintervals of orthogonal component computation of the signal amplitude provided the reduction of errors by about 10 times when the other criteria of the computational process are fixed.
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Keywords: : signal, intelligibility, non-intrusive estimation, denoising.
The calculation of the SNRloss value for the estimated (noisy) signal and the signal, which is obtained by the denoising method proposed by Scalar and Filho, is the main idea for proposed non-reference version of the SNR Loss criterion. The signal is considered reference at the output of squelch method. This is acceptable, as the signal at the output of the denoising method is an estimation of reference signal.
Let`s analyze the dependence of non-intrusive values (which was obtained by the using a denoised signal as a reference and denoted as SNRloss) with the true values SNRloss, which was calculated by the using a pure signal as a reference. Six independent speech fragments were used for modeling. Each fragment was noised by additive white Gaussian noise (AWGN). 230 noisy versions were formed for each fragment: 5 noise implementations for each SNR in the range of -15 to 30 dB. During the simulation SNRloss values were measured for 1380 signals (6 phrases with 230 noise options for each). The set of points in scatter plot was distributed in a way that allows you to make an assumption about the possibility of a linear approximation of the dependence of SNRloss from SNRloss'. On the basis of available data and the method of least squares the relationship can be described by the equation of linear regression:
To verify the proposed non-intrusive method of estimation values SNRloss additional modeling wac conducted. To ensure the reliability the set of speech fragments was selected from different from that set which were used in the first part of the research. Values SNRloss and SNRloss' (for different implementations of noise and SNR) was measured 5520 pairs for 24 speech fragments. The obtained values SNRloss' were substituted into equation (1). Thus, 5520 non-intrusive estimations of values SNRloss was founded. Comparison of non-intrusive estimations with true values of criterion was obtained by using reference method. Proposed method has sufficiently high accuracy of the of non-reference estimation.
When a linear approximation of the average is used, the absolute error value is about 0.008, and its maximum value is equal 0.036. The average value of the relative error is approximately 1.05 %, and its maximum value is 4.72 %. It should be noted that the magnitude of errors can be further reduced through the using a polynomial or piecewise linear approximations.
The obtained results indicate about the prospects of applying the proposed approach to non-intrusive values estimation of SNR Loss criterion. The next step was to test the applicability of the study of this approach to other types of noise, which are more frequently met in solving practical problems. For this purpose, a specialized database of noise "Noisex-92" was used. There are various types of real noise. Since the sampling frequency of speech signal was 8 kHz, the sampling frequency of noise records was also reduced to 8 kHz. The highest standard deviation value and lowest value of the coefficient of determination was observed in the case of finding the linear regression parameters for the noise "Speech babble".
When the modeling with different types of noise confirmed the viability of the proposed non-intrusive technique, the question of its further improvement arose. Analysis of the number of additions and multiplications, which are necessary for estimation of the speech intelligibility, showed that a significant portion of computing operations accounted for the fast Fourier transform (FFT) and inverse FFT (IFFT). However, the used scheme of calculations contains the operations that can was eliminated. However, it is extremely important to bring the parameters of spectral transformation in the block of noise reduction to those that was used in the method of SNR loss because they affect to the reliability of estimations of intelligibility. A proposed modified procedure has a simple structure by eliminating repetitive and mutually exclusive operations and contains one unit for calculating the fast Fourier transform.
The proposed modification was analyzed and compared with the original method by number of computing operations. It was revealed that this modification could reduce the number of operations by approximately 30.5 % according with the original technique.
In addition, the comparison of the procedure speed was made by modeling in Matlab. So a personal computer with the following configuration: Intel (R) Pentium (R) D 930 CPU 3.00GHz, RAM 3,00 GB 400MHz, 64-bit OS Windows 8 was used. The winning in time was 29.9% in the proposed modification.
As simulations have shown, that the proposed modification has a lower accuracy. For example, the mean absolute percentage error (MAPE) increased from approximately 1.3 % to 2.1%. The relative change seems significant in 1.6 times more, but in practice, the above degradation is acceptable. Furthermore, it is possible that further revision of the proposed modification will allow even closer approach to the original accuracy procedure.
Thus, the non-intrusive version for the measure of intelligibility of noisy speech signals SNR loss is proposed. It is based on the application of the original (reference) version of the method SNR loss, squelch method and pair regression. Accuracy of the proposed non-reference measure of intelligibility was explored in the case of impact by different types of noise to the speech signals. The results suggest about the relatively high accuracy of the proposed non-reference estimation methods (the average relative error is 1.05-3.55 %).
Also, a fast version of non-reference technique was presented. From the results of studies, it follows that the modification has a high accuracy, which is inferior to the original, while the speed increased due to a significant reduction in the number of performed computing operations. The proposed modified method can be used for automatic control systems, noise reduction, selection the transmission mode that provides the permissible level of intelligibility in communication systems.
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Stationary surveying and security systems having features of tracking and registration an observed data became widespread. The design of such systems for local event operational surveying is an interesting line of mobile devices development. There are basic requirements such as long distance observation which is met by usage of long-focus lens and wide angled view which is fit by electromechanical drive having high Q-factor.
The article considers double-loop self-regulated circuit which has first order astaticism. It consists of the electronic loop for quick angular displacement response and the external camera drive loop for the wide field of view. Tracking circuits with higher order astaticism are quite sensitive for the fluctuations of camera drive signal, which represent discrete feature of raster image.
The mathematical model has developed in terms of basic system performance criterions. Results with taken into account the influence of image resolution and external actuating factors and different modes of internal tracking circuit are presented. The estimate of tolerant angle velocities and accelerations is given.
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If the signal power is equal or more than noise dispersion in the nonlinear systems, the SF effect may be achieved resulting in . In terms of the selection of weak signals on the background noise this effect is more interesting than the SR one. This was first discovered as a result of the numerical analysis  of the stochastic differential equation in the case of the existence of the harmonic signal and the potential of critical species. The system filtering properties were evaluated using the transfer coefficient in relation to the S/N. The theoretical analysis  has shown that SF occurs as a result of the noise suppression by the signal. The SF effect was also exhibited by the analog simulation [6-8] for the acoustic signals of the rectangular shape. The results  have been experimentally confirmed in  for the nonlinear radio-engineering low-frequency filter of the first order. In  SF was also found in the nonlinear radio-engineering filter of the second order. In  the developed numerical method has shown that SF takes place in the filters of both low and high signal frequencies.
In this paper, based on the  approach developed, the SF effect is analyzed when the signal spectrum has not one but a large number of harmonics. As such a signal, the periodic sequence of rectangular pulses, each period of which contained two bipolar pulses of the same amplitude and duration was chosen. The transfer coefficient was calculated on the basis of the integral over spectrum relations: , where and - modules of the signal and noise harmonics complex amplitudes. The results obtained were compared with the data for the linear system. The study carried out has led to the following conclusions. The main regularities of the SF effect in the case of the harmonic signal are also saved in the case of meandering signals. The numerical analysis of the low-frequency nonlinear filter let us to define the filter parameters area, in which its filtering ability is substantially higher than the similar linear filter has. The q transfer coefficient dependences in reference to the signal/noise ratio show the existence of the signal optimal frequencies, its amplitude and noise power for reaching the highest q values. The optimal conditions of the nonlinear filtering can be achieved for the signals of the power being not significantly greater than the noise dispersion.
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Keywords: computer vision, local binary patterns, object recognition, information technology.
In this paper author, propose the new informative features local binary patterns median pixel's (LBPMP). The author changed the shape and the algorithm for constructing local binary patterns. Author compared the advantages of the new feature in relation to pattern recognition.
The face area is first divided into small regions from which Local binary patterns median pixel's (LBPMP) histograms are extracted and concatenated into a single histogram efficiently representing the face image. The recognition is performed using a nearest neighbor classifier in the computed feature space with function histograms intersection as a dissimilarity measure. Extensive experiments clearly show the superiority of the proposed scheme other local binary pattern on face database tests, which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction.
Features will find their application in various fields of computer vision, such as recognizing traffic signs, identification numbers of vehicles, etc. The presented algorithm for computing informative features can be modified: add new fragments or change the location of the fragments.
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Application of Fourier Transform to the Problem of Distance Measurement by Frequency-Modulated Rangefinder in the Presence of Dispersion
Frequency of the beat signal that corresponds to the maximum of the amplitude spectral density is frequently taken as the frequency estimate. The main drawbacks of this estimate, stemming from the systematic error of Fourier transform, can be mostly eliminated by smoothing weighting functions (WF), and by increasing the range of frequency modulation. However, the distortions caused by dispersion eliminate the advantages of greater modulation range and reduce the effectiveness of the WF.
This work proposes a variant of integral-discrete Fourier transform that eliminates the negative influence of the dispersion.
Taking into account that many WF, including those that can not be represented in terms of elementary functions, may be represented by the adaptive WF (AWF) or approximated by the AWF to any specified precision, we analyze the properties of the proposed transform using the AWF. One of the spectral properties of AWF is that in the absence of the dispersion and noise it theoretically allows to achieve zero error in the estimate of the frequency of a signal.
Analysis of the spectrum distortions of a signal containing samples had shown that when the proposed transform is used, the main lobe at the frequency and the sidelobe at the frequency are not distorted. At the same time, the sidelobes at the normalized frequencies , are strongly distorted and have diminished amplitudes. The distortions of the consecutive sidelobes increase. Also, the spectral properties of AWF are preserved if a signal's frequency are below the Nyquist frequency by 20 … 40%.
Another feature of the proposed transform is the possibility of estimating a frequency above the Nyquist limit due to the significant reduction in the amplitudes of spectral sidelobes at the normalized frequencies , and the consecutive ones. Moreover, in the absence of noise and waveguide losses estimating a signal's frequency may be theoretically possible for the average frequency of the beat signal that is much greater than the Nyquist frequency.
6. Pat. 2435168 RF, G01R 23/16. A method of harmonic analysis of a periodic multifrequency signalÑ / Davydochkin V.M., Davydochkina S.V. / Applied 09.04.2010, Published 27.11.2011 Bulletin ¹33.
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