管道附件影响的管道声发射信号衰减特性

以包含多种附件的液体和气体管道为对象,开展复杂结构管道声发射信号的衰减特性研究。通过采集不同流量压力下管道本体的声发射信号,分析其幅值衰减规律,并用DB8小波包与快速傅里叶变换(FFT)研究管道声发射信号成分变化。通过采集阀门、法兰、流量计等6种管道附件前后的声发射信号并分析衰减规律,发现对声发射信号衰减影响最大的管道部件为电磁流量计。之后重点分析了法兰上螺栓连接紧固程度对信号幅值和频率成分的影响,实验结果表明,螺栓紧固程度越高,声发射信号衰减越小。本文的相关研究结论对工程中进行输油或输气管道进行声发射检测时的传感器布设决策具有集成化的参考价值。     

基于声韵母的汉语语音识别

随着大词汇量连续语音识别技术的发展,越来越多的研究人员选取声韵母作为识别单元。在基于声韵母的汉语连续语音识别中,声韵母基元的准确分割是非常重要的一步。结合汉语发音声学特性,提出了基于声母分割方法和基于段间距离方法相结合的策略。实验结果表明：该方法达到了准确分割的目的。     

Symbol error probability analysis of a multiuser detector for M-PSKsignals based on successive cancellation

A narrow-band multiuser receiver based on successive signal detection and subtraction, is considered. The symbol error probability (SEP) for M-PSK modulated signals is evaluated and analytical approximations for the SEP of the individual signals are presented and compared with results obtained from simulations. For geometrically related signal amplitudes, a constant minimum distance can be guaranteed independent of the number of signals. The required amplitude ratio is shown to be related to M and the number of co-channel signals. Optimizing the transmitted power for the different signals while ensuring the same SEP is then addressed and closed-form expressions of the signal amplitude ratios are derived. The effect of inaccurately estimated signal parameters due to noise is also analyzed. SEP results are presented for synchronous signals in an additive white Gaussian noise environment     

Estimation of vowel recognition with cochlear implant simulations

Because there are many parameters in the cochlear implant (CI) device that can be optimized for individual patients, it is important to estimate a parameter's effect before patient evaluation. In this paper, Mel-frequency cepstrum coefficients (MFCCs) were used to estimate the acoustic vowel space for vowel stimuli processed by the CI simulations. The acoustic space was then compared to vowel recognition performance by normal-hearing subjects listening to the same processed speech. Five CI speech processor parameters were simulated to produce different degree of spectral resolution, spectral smearing, spectral warping, spectral shifting, and amplitude distortion. The acoustic vowel space was highly correlated with normal hearing subjects' vowel recognition performance for parameters that affected the spectral channels and spectral smearing. However, the acoustic vowel space was not significantly correlated with perceptual performance for parameters that affected the degree of spectral warping, spectral shifting, and amplitude distortion. In particular, while spectral warping and shifting did not significantly reshape the acoustic space, vowel recognition performance was significantly affected by these parameters. The results from the acoustic analysis suggest that the CI device can preserve phonetic distinctions under conditions of spectral warping and shifting. Auditory training may help CI patients better perceive these speech cues transmitted by their speech processors.     

用矢量码书和动态内插限制方法解决语音生成逆向解的非唯一性问题

 本文研究运用矢量码书和动态内插限制的方法解决语音生成逆向解的非唯一性问题.表征声道的对数截面积函数的限带傅立叶余弦展开包括了偶次项,采用可变声道长度;频域声学目标增加了前N个零点频率;结合腔包形声学——几何形态映射矢量码书,为声学目标匹配得到过渡音的起始和结尾端点的零点与声道长度,并用内插方法来限定过渡点的零点频率和声道长度,从而解决非唯一性问题.计算机仿真实验表明了本方法的有效性.     

Gabor expansion for adaptive echo cancellation

A good echo cancellation algorithm should have a fast convergence rate, small steady-state residual echo, and less implementation cost. The normalized least mean square (NLMS) adaptive filtering algorithm may not achieve this goal. We show that using the Gabor expansion is a way to achieve this goal. For direct digital signal processing compatibility the Gabor expansion introduced in this paper is for discrete-time signals, although the Gabor expansion also can be used for continuous-time signals. The Gabor expansion can be defined as a discrete-time signal representation in the joint time-frequency domain of a weighted sum of the collection of functions (known as the synthesis functions). There are several design issues in the echo canceller based on the Gabor expansion: the design of the analysis functions for the far-end speech, the design of the analysis functions for the near-end signal containing the echo plus the near-end speech, the design of the adaptive filters in the subsignal path, and the design of the synthesis functions. All the adaptive filters are designed using identical NLMS adaptive filtering algorithms     

Hybrid signal decomposition based on instantaneous harmonic parameters and perceptually motivated wavelet packets for scalable audio coding

The paper presents a complete framework for hybrid representation of audio and speech signals that can be used in coding applications. The parameterization approach is based on the three-part model (sinusoids, transients and noise). The essential contributions of the paper can be summarized as follows: (i) a precise mathematical solution to the problem of instantaneous harmonic parameters estimation that can be applied to nonstationary (amplitude and frequency modulated) signals. The instantaneous harmonic parameters (magnitude, frequency and phase) are calculated as the result of the narrow-band filtering of signals. The frequency-modulated filters synthesis with the closed form impulse response has been proposed. The filter frequency bounds can be determined during the components frequency tracking and can be adjusted according to the fundamental frequency modulations; (ii) a practical technique of instantaneous harmonic analysis and numerical evaluation of its performance; (iii) a new transient parameterization scheme based on matching pursuit with frame-based psychoacoustic optimized wavelet packet dictionary. The choice of most relevant coefficients is based on maximizing the matching between the auditory excitation scalograms of original and modeled signals; (iv) the given hybrid analysis system is applied to speech and audio signals in order to validate the proposed methods.     

Dopplerlet based time-frequency representation via matching pursuits

A new time-frequency representation called Dopplerlet transform, which uses the dilated, translated and modulated windowed Doppler signals as its basis functions, is proposed, and the Fourier transform, short-time Fourier transform (including Gabor transform), wavelet transform, and chirplet transform are formulated in one framework of Dopplerlet transform accordingly. It is proved that the matching pursuits based on Dopplerlet basis functions are convergent, and that the energy of residual signals yielded in the decomposition process decays exponentially. Simulation results show that the matching pursuits with Dopplerlet basis functions can characterize compactly a nonstationary signal.     

Improved vowel onset point detection using epoch intervals

Vowel onset point (VOP) is the instant at which the onset of vowel takes place in the speech signal. Accurate detection of VOP is useful for applications such as consonant–vowel (CV) unit recognition and speech rate modification. Existing VOP detection methods determine VOPs within 40 ms deviation, which may not be suitable for the applications mentioned above. In this paper, a two level approach using multiple sources of evidence is proposed for the accurate detection of VOP. In the proposed method, at the first level, VOPs are identified by combining the complementary evidence from excitation source, spectral peaks and modulation spectrum. At the second level, hypothesized VOPs are verified (genuine or spurious), and their positions are corrected using the uniform epoch intervals present in vowel region. Zero frequency filter method is used to determine the epoch locations in speech. Performance of the proposed method is analyzed using TIMIT database, and compared with the recent method which uses the combination of evidence from excitation source, spectral peaks and modulation spectrum. Using the proposed method about 85% of VOPs are detected within 10 ms deviation.     

基于EMD的声发射管道泄漏检测研究

针对管道泄漏声发射检测信号的非平稳特征,该文提出了基于经验模态分解(EMD)的声发射信号分析方法。该信号分析法将管道泄漏产生的复杂声发射信号分解成有限个固有模态信号(IMF),使Hilbert-Huang变换(HHT)的瞬时频率具有了实际物理意义,提高了管道泄漏检测的定位精度。结果表明,HHT法能准确描述声发射波形信号的非线性、非平稳时变特征,是声发射信号时频分析的有效工具。     

A novel speech-processing strategy incorporating tonal information for cochlear implants

Good performance in cochlear implant users depends in large part on the ability of a speech processor to effectively decompose speech signals into multiple channels of narrow-band electrical pulses for stimulation of the auditory nerve. Speech processors that extract only envelopes of the narrow-band signals (e.g., the continuous interleaved sampling (CIS) processor) may not provide sufficient information to encode the tonal cues in languages such as Chinese. To improve the performance in cochlear implant users who speak tonal language, we proposed and developed a novel speech-processing strategy, which extracted both the envelopes of the narrow-band signals and the fundamental frequency (F0) of the speech signal, and used them to modulate both the amplitude and the frequency of the electrical pulses delivered to stimulation electrodes. We developed an algorithm to extract the fundatmental frequency and identified the general patterns of pitch variations of four typical tones in Chinese speech. The effectiveness of the extraction algorithm was verified with an artificial neural network that recognized the tonal patterns from the extracted F0 information. We then compared the novel strategy with the envelope-extraction CIS strategy in human subjects with normal hearing. The novel strategy produced significant improvement in perception of Chinese tones, phrases, and sentences. This novel processor with dynamic modulation of both frequency and amplitude is encouraging for the design of a cochlear implant device for sensorineurally deaf patients who speak tonal languages.     

Integration of acoustic and visual speech signals using neuralnetworks

Results from a series of experiments that use neural networks to process the visual speech signals of a male talker are presented. In these preliminary experiments, the results are limited to static images of vowels. It is demonstrated that these networks are able to extract speech information from the visual images and that this information can be used to improve automatic vowel recognition. The structure of speech and its corresponding acoustic and visual signals are reviewed. The specific data that was used in the experiments along with the network architectures and algorithms are described. The results of integrating the visual and auditory signals for vowel recognition in the presence of acoustic noise are presented     

镀镍钢带拉伸断裂过程的声发射特征

用声发射监测镀镍钢带拉伸断裂过程,对PCI-2声发射系统采集到的声发射信号进行参数分析,结合拉伸机的测试结果,研究声发射信号的特征参数与试件拉伸力学行为间的相关性。研究结果表明,拉伸断裂过程中试样声发射信号的振铃计数、能量、持续时间、幅值等声发射特征参数能表征试样损伤过程,损伤随应变的变化规律;试样变形至屈服阶段、颈缩断裂阶段时,声发射信号的能量和振铃计数都会增加;试样开始颈缩时所发出的声发射信号的幅值主要分布在43～79dB;声发射技术是一种监测镀镍钢带失效行之有效的方法。。     

一种采用高重复频率激光进行水声通信的方法

为研究空中平台与水下目标之间的激光声通信技术,提出了一种热膨胀机制下采用高重复频率激光进行水声通信的方法(重复频率法)。理论推导了高重复频率激光产生窄带声信号的过程,并通过实验测量进行了验证。利用高重复频率激光产生了频移键控(n-FSK)和多频移键控(n-MFSK)两种频移键控信号,结合现有激光器的技术指标,针对不同调制信号的水下通信距离、占用带宽、传输速率进行了分析计算,并与现有的方法(长脉冲法)进行了比较。结果表明,n-MFSK调制的传输速率比n-FSK调制的更快,频带利用率更高,但水下通信距离不及n-FSK调制;同为n-FSK调制,重复频率法的水下通信距离为1000m,比长脉冲法高40%,通信性能优于长脉冲法。     

Discrimination of pathological voices using a time-frequency approach

Acoustical measures of vocal function are routinely used in the assessments of disordered voice, and for monitoring the patient's progress over the course of voice therapy. Typically, acoustic measures are extracted from sustained vowel stimuli where short-term and long-term perturbations in fundamental frequency and intensity, and the level of "glottal noise" are used to characterize the vocal function. However, acoustic measures extracted from continuous speech samples may well be required for accurate prediction of abnormal voice quality that is relevant to the client's "real world" experience. In contrast with sustained vowel research, there is relatively sparse literature on the effectiveness of acoustic measures extracted from continuous speech samples. This is partially due to the challenge of segmenting the speech signal into voiced, unvoiced, and silence periods before features can be extracted for vocal function characterization. In this paper we propose a joint time-frequency approach for classifying pathological voices using continuous speech signals that obviates the need for such segmentation. The speech signals were decomposed using an adaptive time-frequency transform algorithm, and several features such as the octave max, octave mean, energy ratio, length ratio, and frequency ratio were extracted from the decomposition parameters and analyzed using statistical pattern classification techniques. Experiments with a database consisting of continuous speech samples from 51 normal and 161 pathological talkers yielded a classification accuracy of 93.4%.     

Spotting and Recognition of Consonant-Vowel Units from Continuous Speech Using Accurate Detection of Vowel Onset Points

In this paper, we propose an efficient approach to spotting and recognition of consonant-vowel (CV) units from continuous speech using accurate detection of vowel onset points (VOPs). Existing methods for VOP detection suffer from lack of high accuracy, spurious VOPs, and missed VOPs. The proposed VOP detection is designed to overcome most of the shortcomings of the existing methods and provide accurate detection of VOPs for improving the performance of spotting and recognition of CV units. The proposed method for VOP detection is carried out in two levels. At the first level, VOPs are detected by combining the complementary evidence from excitation source, spectral peaks, and modulation spectrum. At the second level, hypothesized VOPs are verified (genuine or spurious), and their positions are corrected using the uniform epoch intervals present in the vowel regions. The spotted CV units are recognized using a two-stage CV recognizer. Two-stage CV recognition system consists of hidden Markov models (HMMs) at the first stage for recognizing the vowel category of a CV unit and support vector machines (SVMs) for recognizing the consonant category of a CV unit at the second stage. Performance of spotting and recognition of CV units from continuous speech is evaluated using Telugu broadcast news speech corpus.     

Distortionless demodulation of narrow-band single-sideband angle modulated signals

The problem of distortionless demodulation of a narrow-band single-sideband angle modulated signal is considered. The receiver uses a standard angle detector followed by a low-pass filter. The class of modulating signals is Zakai's class of band-limited functions and processes. It is shown that the demodulation of these baseband signals is distortionless under an appropriate boundedness constraint on the signals.     

Acquirement and enhancement of remote speech signals

To address the challenges of non-cooperative and remote acoustic detection, an all-fiber laser Doppler vibrometer (LDV) is established. The all-fiber LDV system can offer the advantages of smaller size, lightweight design and robust structure, hence it is a better fit for remote speech detection. In order to improve the performance and the efficiency of LDV for long-range hearing, the speech enhancement technology based on optimally modified log-spectral amplitude (OM-LSA) algorithm is used. The experimental results show that the comprehensible speech signals within the range of 150 m can be obtained by the proposed LDV. The signal-to-noise ratio (SNR) and mean opinion score (MOS) of the LDV speech signal can be increased by 100% and 27%, respectively, by using the speech enhancement technology. This all-fiber LDV, which combines the speech enhancement technology, can meet the practical demand in engineering.     

Encoding frequency modulation to improve cochlear implant performance in noise

Different from traditional Fourier analysis, a signal can be decomposed into amplitude and frequency modulation components. The speech processing strategy in most modern cochlear implants only extracts and encodes amplitude modulation in a limited number of frequency bands. While amplitude modulation encoding has allowed cochlear implant users to achieve good speech recognition in quiet, their performance in noise is severely compromised. Here, we propose a novel speech processing strategy that encodes both amplitude and frequency modulations in order to improve cochlear implant performance in noise. By removing the center frequency from the subband signals and additionally limiting the frequency modulation's range and rate, the present strategy transforms the fast-varying temporal fine structure into a slowly varying frequency modulation signal. As a first step, we evaluated the potential contribution of additional frequency modulation to speech recognition in noise via acoustic simulations of the cochlear implant. We found that while amplitude modulation from a limited number of spectral bands is sufficient to support speech recognition in quiet, frequency modulation is needed to support speech recognition in noise. In particular, improvement by as much as 71 percentage points was observed for sentence recognition in the presence of a competing voice. The present result strongly suggests that frequency modulation be extracted and encoded to improve cochlear implant performance in realistic listening situations. We have proposed several implementation methods to stimulate further investigation. Index Terms-Amplitude modulation, cochlear implant, fine structure, frequency modulation, signal processing, speech recognition, temporal envelope.     

Statistical AM-FM models, extended Kalman filter demodulation,Cramer-Rao bounds, and speech analysis

A stochastic dynamical system model for describing time signals that are jointly amplitude (AM) and frequency (FM) modulated is presented. The signal is assumed to be bandpass, perhaps originating from a filter bank applied to a broadband signal, and includes the constraint that the magnitude of the complex baseband signal is positive. Motivated by speech processing and the desire for narrowband modulating signals, time is divided into frames, and the modulating signals are smoothly interpolated across each frame. The model allows a detailed characterization of the bandwidth of the modulating signals and the statistical character of the measurement noise. An adaptive estimation algorithm based on extended Kalman filtering ideas for extracting the modulating signals from the measured signal is described and demonstrated on both voiced and unvoiced speech signals. The Cramer-Rao bound on the performance of any estimator is computed