基于小波变换的长输管道泄露音波信号检测方法研究

长输管道的泄露会造成严重后果,因此对其进行泄露信号的检测是十分必要的。针对该问题,提出基于小波变换的长输管道泄露音波信号检测方法研究。基于小波变换的方法对音波信号处理进行设计,利用小波变换多分辨率和时频局域化的优势,提出一种全新的长输管道泄露音波信号检测方法。通过与传统检测方法对比实验,证明新的检测方法能够更加精准地找出泄露异常点的位置,提高维修工作效率。

A new adaptive filtering algorithm for systems with multiplicative noise

Presented here is a new adaptive state filtering algorithm for systems with multiplicative noise. This algorithm estimates the vector state of the system and the statistics of noise when all the statistics of noise are unknown. This filtering algorithm is a simple recursive structure. A simulation example is presented which demonstrates the effectiveness of this filtering algorithm.

Filter algorithm based on cochlear mechanics and neuron filter mechanism and application on enhancement of audio signals

A filter algorithm based on cochlear mechanics and neuron filter mechanism is proposed from the view point of vibration.It helps to solve the problem that the non-linear amplification is rarely considered in studying the auditory filters.A cochlear mechanical transduction model is built to illustrate the audio signals processing procedure in cochlea,and then the neuron filter mechanism is modeled to indirectly obtain the outputs with the cochlear properties of frequency tuning and non-linear amplification.The mathematic description of the proposed algorithm is derived by the two models.The parameter space,the parameter selection rules and the error correction of the proposed algorithm are discussed.The unit impulse responses in the time domain and the frequency domain are simulated and compared to probe into the characteristics of the proposed algorithm.Then a 24-channel filter bank is built based on the proposed algorithm and applied to the enhancements of the audio signals.The experiments and comparisons verify that,the proposed algorithm can effectively divide the audio signals into different frequencies,significantly enhance the high frequency parts,and provide positive impacts on the performance of speech enhancement in different noise environments,especially for the babble noise and the volvo noise.

Cylindrical Guided Wave Signals for Underground Pipe Inspection using Different Continuous Wavelet Mother Functions

Over the past decade, wavelets provided a powerful and flexible set of tools for handling fundamental problems in science and engineering. Wavelet analyses are being used for solving problems in different engineering areas like audio de-noising, signal compression, object detection, image decomposition, speech recognition etc. Wavelet analysis employs orthonormal as well as non-orthonornal functions. This research investigates the effectiveness of wavelet analysis in detecting defects in underground steel pipe networks. Continuous Wavelet Transforms （CWT） has been performed on the received signals of cylindrical guided waves. Cylindrical Guided waves are generated and propagated through the pipe wall boundaries in a pitch-catch system. Piezo-electric transducers are used to generate as well as receive guided waves. Several mother wavelet functions such as Daubechies, Symlet, Coiflet and Meyer have been used for the Continuous Wavelet Transform to investigate the most suitable function for defect detection. This research also investigates the effect of surrounding soil on wavelet transforms for different mother wavelet functions.

Speech-stream detection in short-wave channel based on empirical mode decomposition and higher-order statistics

To capture the presence of speech embedded in nonspeech events and background noise in shortwave non-cooperative communication, an algorithm for speech-stream detection in noisy environments is presented based on Empirical Mode Decomposition （EMD） and statistical properties of higher-order cumulants of speech signals. With the EMD, the noise signals can be decomposed into different numbers of IMFs. Then, the fourth-order cumulant （ FOC ） can be used to extract the desired feature of statistical properties for IMF components. Since the higher-order eumulants are blind for Gaussian signals, the proposed method is especially effective regarding the problem of speech-stream detection, where the speech signal is distorted by Gaussian noise. With the self-adaptive decomposition by EMD, the proposed method can also work well for non-Gaussian noise. The experiments show that the proposed algorithm can suppress different noise types with different SNRs, and the algorithm is robust in real signal tests.

SINGLE TONE JAMMING ELIMINATION SCHEME OF MULTICARRIER SPREAD SPECTRUM SYSTEM

The proposed scheme is based on Discrete Fourier Transform (DFT) domain processing. The key technology of this scheme is jamming parameters' accurate estimation and jamming reconstruction. Compared with the 'threshold exciser' scheme the proposed scheme can eliminate more jamming energy on the whole frequency band with the minimum loss of useful signal energy. As shown in the research and simulation, the proposed scheme is much better than the 'threshold exciser' scheme, especially in the case of high power jamming whereas the 'threshold exciser' scheme might be invalid.

Extraction of Partial Discharge Acoustic Signal by Wavelet Transform with Teager′s Energy Operator

To develop a measurement system for monitoring partial discharge （PD） without the effect of external interferences,an algorithm of PD signal extraction based on wavelet transform with Teager＇s energy operators was presented. Acoustic signal generated by PD was selected to remove excessive interfering signals and electromagnetic interferences. Acoustic signals were collected and decomposed into I0 levels by wavelet transform into approximation and detail components. “Daubechies 25” was proved to be the most suitable mother wavelet for the extraction of PD acoustic signals. Compared with conventional wavelet denoising method, Teager＇s energy operators were adopted to the PD signal reconstruction and the signal to noise ratio was in creased by 20%-25% inthe experiment,without lost in energy and pulse amplitude.

Audio Zero-Watermark Scheme Based on Discrete Cosine Transform-Discrete Wavelet TransformSingular Value Decomposition

Traditional watermark embedding schemes inevitably modify the data in a host audio signal and lead to the degradation of the host signal.In this paper,a novel audio zero-watermarking algorithm based on discrete wavelet transform(DWT),discrete cosine transform(DCT),and singular value decomposition(SVD) is presented.The watermark is registered by performing SVD on the coefficients generated through DWT and DCT to avoid data modification and host signal degradation.Simulation results show that the proposed zero-watermarking algorithm is strongly robust to common signal processing methods such as requantization,MP3 compression,resampling,addition of white Gaussian noise,and low-pass filtering.

A Parametric Autoregressive Model for the Extraction of Electric Network Frequency Fluctuations in Audio Forensic Authentication

This paper proposes a new method for extracting ENF （electric network frequency） fluctuations from digital audio recordings for the purpose of forensic authentication. It is shown that the extraction of ENF components from audio recordings is realizable by applying a parametric approach based on an AR （autoregressive） model. The proposed method is compared to the existing STFT （short-time Fourier transform） based ENF extraction method. Experimental results from recorded electrical grid signals and recorded audio signals show that the proposed approach can improve the time resolution in the extracted ENF fluctuations and improve the detection of tampering with short alterations in longer audio recordings.

Compression and reconstruction of speech signals based on compressed sensing

Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing,furthermore,the threshold is set to improve the sparseness.Then an adaptive subgradient projection method(ASPM)is adopted for speech reconstruction in compressed sensing.Meanwhile,mechanism which adaptively adjusts inflation parameter in different iterations has been designed for fast convergence.Theoretical analysis and simulation results conclude that this algorithm has fast convergence,and lower reconstruction error,and also exhibits higher robustness in different noise intensities.

Hilbert-Huang变换在管道泄漏监测系统中的应用

音波管道泄漏监测系统利用管道泄漏产生的音波信号进行泄漏报警定位。为了能够及时发现泄漏并准确定位,需要从管道的背景噪音中检测到音波信号的微弱瞬态变化以及音波传感器接收到信号瞬态变化的准确时刻。Hilbert-Huang变换作为一种时频分析算法,能够同时获得较高的频率分辨率和时间分辨率,利用该特性,选取兰成渝成品油管道进行模拟试验,使用Hilbert-Huang变换对泄漏音波信号进行处理,可以获得信号的时频分布图,分析信号每个时刻的频率特性,进而得到泄漏音波信号到达传感器的准确时刻,并据此计算泄漏点位置。

Using psychophysiological measures to recognize personal music emotional experience

Music can trigger human emotion.This is a psychophysiological process.Therefore,using psychophysiological characteristics could be a way to understand individual music emotional experience.In this study,we explore a new method of personal music emotion recognition based on human physiological characteristics.First,we build up a database of features based on emotions related to music and a database based on physiological signals derived from music listening including EDA,PPG,SKT,RSP,and PD variation information.Then linear regression,ridge regression,support vector machines with three different kernels,decision trees,k-nearest neighbors,multi-layer perceptron,and Nu support vector regression(NuSVR)are used to recognize music emotions via a data synthesis of music features and human physiological features.NuSVR outperforms the other methods.The correlation coefficient values are 0.7347 for arousal and 0.7902 for valence,while the mean squared errors are 0.023 23 for arousal and0.014 85 for valence.Finally,we compare the different data sets and find that the data set with all the features(music features and all physiological features)has the best performance in modeling.The correlation coefficient values are 0.6499 for arousal and 0.7735 for valence,while the mean squared errors are 0.029 32 for arousal and0.015 76 for valence.We provide an effective way to recognize personal music emotional experience,and the study can be applied to personalized music recommendation.