Detection of Mitral Valve Diseases by Bicoherence Analysis of Heart Sound Signals

The analysis of 10 normal and 51 mitral valve pathology making a total of 61 heart sound signals that were obtained with Littmann 4100 Digital Stethoscope were conducted in this study.Following the recorded sound signals were denoised by using wavelet filters,the signals were applied bicoherence analysis that is an high order spectral analysis method.It has been demonstrated that varieties of mitral valve pathology could be determined by three-dimensional surfaces of bicoherence and maximum bicoherence values.

Auditory detection of sound signals with complex time-frequency characteristics

The mechanism of the human auditory system in detecting sound signals with complex time frequency charcteristics in a white noise background was reviewed and discussed.The efficiency of such auditory detection was assessed by comparing it with that of parallel visual detection of the output of an analogous model displayed on the oscilloscope screen. The results suggest that the detection model of the human auditory system is quite similar to a tone correlator when the time frequency characteristics of the signal are known and to an energy detector when the signal is unknown. The relationship between the threshold signal to noise ratio and the signal duration is derived for different time frequency characteristics.

Characteristics analyzing and parametric modeling of the arc sound in CO_2 GMAW for on-line quality monitoring

For on-line monitoring of welding quality, the characteristics of the arc sound signals in short circuit CO2 GMAW were analyzed in the time and frequency domains. The arc sound presents a series of ringing-like oscillations that occur at the end of short circuit i. e. the moment of arc re-ignition, and distributes mainly in the frequency band below 10 kHz. A concept of the arc tone channel and its equivalent electrical model were suggested, which is considered a time-dependent distributed parametric system of which the transmission properties depend upon the geometric and physical characteristics of the arc and surroundings, and is excited by the sound source results from the change of arc energy so that results in arc sound. The linear prediction coding （ LPC ） model is an estimation of the tone channel. The radial basis function （ RBF ） neural networks were built for on-line pattern recognition of the gas-lack in welding, in which the input vectors were formed with the LPC coefficients. The test results proved that the LPC model of arc sound and the RBF networks are feasible in on-line quality monitoring.

Condition Monitoring of Roller Bearing by K-star Classifier andK-nearest Neighborhood Classifier Using Sound Signal

Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.

DEVELOPMENT AND USAGE OF SIGNAL PR OCESSING AND SOUND SITIMULATING SYSTEM CONTROLLED BY COMPUTER

In hearing physiological experiments and clinic tests,we need not only a signal processing system,but also a synchronous sound stimulator’ Most of stimulators we are now using are function generators which are independent to processing units,and can be controlled only by hand. Although some of them have ports through which they can be controlled by computer,but as they are designed for industrial aims,not for hearing research,most of them can’t generate the special waveforms we need. We use the TDT signal processing system and develop a software package have both usage. On the interface of the program we can control the sampling parameters and generate stimulating waveforms’

Propagation of quasi-periodic random sound pulse sequence signals in shallow-water waveguides

It is an efficient method to model ship radiated noise as quasi-periodic random sound pulse sequences. Based on the model, this paper discusses the characteristics change of ship noise after through shallow-water waveguides. Theoretical analysis and numerical simulation show that random waveguides and multi-path effects can bring much additional transmission loss for the noise line spectra, demonstrating the instability of ship radiated noise line spectra to a certain extent, and providing some theoretical support for advanced studies of ship radiated noise.

Prediction of propeller non-cavitation noise by superimposing shifted sound signal from an isolated blade

The time-cost of the propeller non-cavitation noise prediction can be greatly re- duced by the isolated blade method, which is validated via hybrid URANS and acoustic analogy, followed by the acoustic characteristics of propeller in time domain are analyzed. Firstly, we predicted the sound of the E779A propeller operating in uniform inflow and found a typical periodic characteristic of the sound pressure distribution on propeller blade as well as the sound signal of the receiver, and the result by the superimposing shifted sound signal from an isolated blade （isolated blade method） agreed well with the result by the integration on total blades, which validated the credibility of the isolated blade method in uniform inflow. Finally, we pre- dicted the sound of a propeller running in the wake of submarine by the isolated blade method, and the result also agreed well with the result by the integration on total blades, which further indicated that the isolated blade method was also applicable for the non-cavitation noise prediction of the propeller running in non-uniform inflow. The noise prediction of the counter-rotating propeller, the pump-jet can also benefit from this method.

Application of wavelet scale correlation filtering and its improved algorithm in signal processing with a spark sound source

It is seriously interfered by ship noise when analyzing and extracting broadband spark sound source signal. In the energy concentrated domain which is below 5 kHz, the traditional scale correlation filtering algorithm, which is based on adjacent-scale correlation, has limited anti-interference ability due to the low signal-to-noise ratio （SNR） and similar Lipschitz exponent characteristic of each other. However, because different frequency bands of the broadband electric spark signal have different noise interferences, the filtering algorithm based on adjacent-scale correlation is adapted to high SNR and small-scale high-frequency wavelet coefficients filtering; the filtering algorithm based on cross-scale correlation is adapted to low SNR and large-scale low-frequency wavelet coefficients filtering, and the threshold coefficient selection method had been corrected in the algorithm. It is shown that the filtering algorithm has a good filtering effect and extracts the broadband spark sound source signal effectively; it is applicable to broadband underwater acoustic signM processing in the presence of narrow-band strong interference background noise.

Research on fault diagnosis of a railway point machine based on a multi-entropy feature extraction method and support vector machine

A new fault diagnosis method is proposed to effectively extract the fault features of the sound signal of typical faults of ZDJ9 railway point machines.A multi-entropy feature extraction method is proposed by combing multi-scale permutation entropy and wavelet packet entropy.Firstly,empirical mode decomposition is performed on sound signals to obtain modal components with different time scales.Then,multi-scale permutation entropy is extracted from these components.Meanwhile,the wavelet packet entropy of the sound signals of these sensitive nodes is obtained by analysing the reconstructed signals of the last layer nodes.Since the multi-scale permutation entropy and the wavelet packet entropy can distinguish the subtle features of the signal,the subtle features of the information among the high-dimensional features,ReliefF is utilized.Finally,a support vector machine(SVM)is used to judge the original sismal can be obtained as the feature vector of the 2DJ9 iway point mnchine in ditterent states,To reduce the redundant fault type of a ZDJ9 rilway point machine.