The effect of electrode arrangement on spectral distance measuresfor discrimination of EMG signals

The proper electrode placement in applying cepstral coefficients for electromyogram (EMG) signature discrimination was investigated. The authors measured EMG signals of different motions with two electrode arrangements simultaneously. Electrode pairs were located separately on dominant muscles (S-type arrangement) and closely in the region between muscles (C-type arrangement). The application of the cepstral method to signals derived from a C-type arrangement did not achieve the same discrimination as with a S-type arrangement. The authors used a simplified model to elucidate the poor performance in C-type signals. The bandwidth of signals obtained from S-type placement is wider than that from C-type. Narrower bandwidth decreases the importance of the more discriminative parts for both autoregressive (AR) and cepstral methods. The cepstral method is more sensitive to such variation, so the degradation in performance is more serious for the cepstral method. Second, the amplitude of C-type signal is lower than the S-type; therefore, the C-type signal is more sensitive to the disturbance of noise, especially in the high-frequency band. As high-frequency noise increases, the spectral difference between different EMG signals is gradually dominated by the low-frequency part, which is more informative. Thus, the performances of both methods are improved with increasing high-frequency noise. The improving rate of the AR method is faster than the cepstral method; therefore, its discriminative efficiency may exceed the cepstral method with C-type arrangement     

Filtering of Filter‐Bank Energies for Robust Speech Recognition

We propose a novel feature processing technique which can provide a cepstral liftering effect in the log‐spectral domain. Cepstral liftering aims at the equalization of variance of cepstral coefficients for the distance‐based speech recognizer, and as a result, provides the robustness for additive noise and speaker variability. However, in the popular hidden Markov model based framework, cepstral liftering has no effect in recognition performance. We derive a filtering method in log‐spectral domain corresponding to the cepstral liftering. The proposed method performs a high‐pass filtering based on the decorrelation of filter‐bank energies. We show that in noisy speech recognition, the proposed method reduces the error rate by 52.7% to conventional feature.     

Detection of landmines and underground utilities from acoustic and GPR images with a cepstral approach

This paper introduces a cepstral approach for the automatic detection of landmines and underground utilities from acoustic and ground penetrating radar (GPR) images. This approach is based on treating the problem as a pattern recognition problem. Cepstral features are extracted from a group of images, which are transformed first to 1-D signals by lexicographic ordering. Mel-frequency cepstral coefficients (MFCCs) and polynomial shape coefficients are extracted from these 1-D signals to form a database of features, which can be used to train a neural network with these features. The target detection can be performed by extracting features from any new image with the same method used in the training phase. These features are tested with the neural network to decide whether a target exists or not. The different domains are tested and compared for efficient feature extraction from the lexicographically ordered 1-D signals. Experimental results show the success of the proposed cepstral approach for landmine detection from both acoustic and GPR images at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction.     

等方差加权倒谱失真测度及其在说话人识别中的应用

本文根据倒谱系数矢量在特征空间的统计分布特性,提出了一种新的等方差加权倒谱失真测度,这种测度的加权函数充分刻画了语音倒谱矢量在特征空间分布的精细结构,从而有效地辨识不同讲话者的特征,实验表明,和常规的欧氏距离及方差倒数加权距离等相比,本文所提的失真测度能显著提高基于矢量量化的说话人识别的正识率。     

Isolated Mandarin syllable recognition using segmental features

A segment-based speech recognition scheme is proposed. The basic idea is to model explicitly the correlation among successive frames of speech signals by using features representing contours of spectral parameters. The speech signal of an utterance is regarded as a template formed by directly concatenating a sequence of acoustic segments. Each constituent acoustic segment is of variable length in nature and represented by a fixed dimensional feature vector formed by coefficients of discrete orthonormal polynomial expansions for approximating its spectral parameter contours. In the training, an automatic algorithm is proposed to generate several segment-based reference templates for each syllable class. In the testing, a frame-based dynamic programming procedure is employed to calculate the matching score of comparing the test utterance with each reference template. Performance of the proposed scheme was examined by simulations on multi-speaker speech recognition for 408 highly confusing isolated Mandarin base-syllables. A recognition rate of 81.1% was achieved for the case using 5-segment, 8-reference template models with cepstral and delta-cepstral coefficients as the recognition features. It is 4.5% higher than that of a well-modelled 12-state, 5-mixture CHMM method using cepstral, delta cepstral, and delta-delta cepstral coefficients     

Wavelet based robust sub-band features for phoneme recognition

Wavelet transform has been found to be an effective tool for the time-frequency analysis of non-stationary and quasi-stationary signals. Recent years have seen wavelet transform being used for feature extraction in speech recognition applications. In the paper a sub-band feature extraction technique based on an admissible wavelet transform is proposed and the features are modified to make them robust to additive white Gaussian noise. The performance of this system is compared with the conventional mel frequency cepstral coefficients (MFCC) under various signal to noise ratios. The recognition performance based on the eight sub-band features is found to be superior under the noisy conditions compared with MFCC features.     

Parametric representation and screening of knee jointvibroarthrographic signals

The authors have been investigating analysis of knee joint vibration or vibroarthrographic (VAG) signals as a potential tool for noninvasive diagnosis and monitoring of cartilage pathology. In this paper, they present a comprehensive comparative study of different parametric representations of VAG signals. Dominant poles and cepstral coefficients were derived from autoregressive models of adaptively segmented VAG signals. Signal features and a few clinical features were used as feature vectors in pattern classification experiments based on logistic regression analysis and the leave-one-out method. The results using 51 normal and 39 abnormal signals indicated the superior performance of cepstral coefficients in VAG signal classification with an accuracy rate of 75.6%. With 51 normal and 20 abnormal signals limited to chondromalacia patella, cepstral coefficients again gave the highest accuracy rate of 85.9%     

Recognition of Electromyographic Signals Using Cascaded Kernel Learning Machine

Electromyographic (EMG) signals recognition is a complex pattern recognition problem due to its property of large variations in signals and features. This paper proposes a novel EMG classifier called cascaded kernel learning machine (CKLM) to achieve the goal of high-accuracy EMG recognition. First, the EMG signals are acquired by three surface electrodes placed on three different muscles. Second, EMG features are extracted by autoregressive model (ARM) and EMG histogram. After the feature extraction, the CKLM is performed to classify the features. CKLM is composed of two different kinds of kernel learning machines: generalized discriminant analysis (GDA) algorithm and support vector machine (SVM). By using GDA, both the goals of the dimensionality reduction of input features and the selection of discriminating features, named kernel FisherEMG, can be reached. Then, SVM combined with one-against-one strategy is executed to classify the kernel FisherEMG. By cascading SVM with GDA, the input features will be nonlinearly mapped twice by radial-basis function (RBF). As a result, a linear optimal separating hyperplane can be found with the largest margin of separation between each pair of postures' classes in the implicit dot product feature space. In addition, we develop a digital signal processor (DSP)-based EMG classification system for the control of a multi-degrees-of-freedom prosthetic hand for the practical implementation. Based on the clinical experiments, the results show that the proposed CKLM is superior to other frequently used methods, such as k-nearest neighbor algorithm, multilayer neural network, and SVM. The best EMG recognition rate 93.54% is obtained by CKLM.     

Application of kernel methods in signals modulation classification

A new approach to common signals classification of relevance vector machine(RVM) was presented and two signal classifiers based on kernel methods of support vector machine(SVM) and RVM were compared and analyzed.First several robust features of signals were extracted as the input of classifiers,then the kernel thought was used to map feature vectors impliedly to the high dimensional feature space,and multi-class RVM and SVM classifiers were designed to complete AM,CW,SSB,MFSK and MPSK signals recognition.Simulation result showed that when chose proper parameter,RVM and SVM had comparable accuracy but RVM had less learning time and basis functions.The classification speed of RVM is much faster than SVM.     

Evaluation of a Multifunctional Hand Prosthesis System Using EMG Controlled Animation

In this communication, we present an on-line real-time simulation system used to evaluate a new method of controlling a multifunctional hand prosthesis. The system employs two computers. Acquisition, analysis, and classification of EMG signals are performed by a minicomputer, and the animation model of the hand prosthesis which is displayed on a monitor TV is controlled by a microcomputer. From three EMG signals picked up from the most suitable muscles of the forearm, three amplitude and three frequency components are derived. Six voluntary movements are then classified by the use of a pattern recognition technique based on the linear discriminant analysis method. Besides the six basic motions, the model can also make eight combined motions using the method of selecting control signals. The experimental results tested on healthy adults indicated that recognition rate was above 90 percent, and nearly 100 percent in some of the better cases. Then the subjects could easily operate the model on the TV screen.     

Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal

To explore the influence of the fusion of different features on recognition, this paper took the electromyography(EMG) signals of rectus femoris under different motions(walk, step, ramp, squat, and sitting) as samples, linear features(time-domain features(variance(VAR) and root mean square(RMS)), frequency-domain features(mean frequency(MF) and mean power frequency(MPF)), and nonlinear features(empirical mode decomposition(EMD)) of the samples were extracted. Two feature fusion algorithms, the s...     

基于LPCC和MFCC参数的病理嗓音识别研究

自动检测正常嗓音和病理嗓音的关键是选出有效的特征参数,并对其进行优化得到简单易实现的参数。同时选择合适的识别模型对正常嗓音和病理嗓音进行识别以得到最好的识别率。为了能实时、便利地检测正常嗓音和病理嗓音,这里提出了线性预测倒谱系数(LPCC)和MEL频率倒谱系数(MFCC)声学特征参数,采用动态时间规整(DTW)算法进行识别,实验结果表明该模型的识别率可达到90%以上,且MFCC方法优于LPCC。     

基于EMD的音频信号检索

介绍了一种基于Mel倒谱参数和Earth Mover’s Distance（EMD）度量的音频检索方法，它利用了人耳的感知特性，并结合特征分布之间的距离度量EMD，获得了良好的性能，具有广阔的发展前景。     

作为说话人识别特征参量的MFCC的提取过程

说话人识别是人的个体特征识别中的一个重要分支,在实际生活中已得到广泛应用。而人的听觉系统是一个比较理想的说话人识别系统,MFCC(Mel倒谱系数)模拟了人的听觉特性,是符合人听觉特性的语音特征参量,在实际应用中取得了较高的识别率。文中通过一个卷积同态系统简单介绍了语音信号的倒谱分析方法,并通过对Mel频率刻度得到符合人听觉特性的Mel频率等效滤波器组,最后介绍了MFCC求取的一般过程和算法。     

改进BP神经网络的EMG手指运动识别

在基于肌电信号(EMG)手指运动的模式识别中,稳定性和识别率是两个主要问题,为此提出了一种新的EMG模式识别算法。该算法采用现代信号处理理论中的AR模型和改进的BP神经网络相结合的算法,有效的解决了BP网络识别中落入局部极值问题。进行试验,将提取到的特征值输入MATLAB建立一个改进多层BP神经网络,识别三个不同类型的手指运动。实验表明,改进BP算法较传统BP算法获得了更高的识别精度,达到94%左右。     

自动识别数字调制方式的一种方法

提出了一种对经符号成形的数字通信信号进行调制方式自动识别的方案,该方案将数字已调信号的瞬时特征与功率谱特性相结合,设计了一组对信号信噪比不敏感的特征参数。通过提取经升余弦滤波成形的2FSK等六种常用的数字调制信号的5个特征参数,分别采用决策树和神经网络分类器进行调制方式自动识别。仿真表明,当信噪比为11dB时,采用决策树分类器对除2ASK外的5种信号的识别率在95%以上,神经网络分类器此时对所有信号的识别率达到了98%以上,证明了该方案的有效性和可行性。     

A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control

Electromyographic (EMG) pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study was to develop an efficient feature- projection method for EMG pattern recognition. To this end, a linear supervised feature projection is proposed that utilizes a linear discriminant analysis (LDA). First, a wavelet packet transform (WPT) is performed to extract a feature vector from four-channel EMG signals. To dimensionally reduce and cluster the WPT features, an LDA, then, incorporates class information into the learning procedure, and identifies a linear matrix to maximize the class separability for the projected features. Finally, a multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of the LDA for WPT features, the LDA is compared with three other feature-projection methods. From a visualization and quantitative comparison, it is shown that the LDA produces a better performance for the class separability, plus the LDA-projected features improve the classification accuracy with a short processing time. A real-time pattern-recognition system is then implemented for a multifunction myoelectric hand. Experiments show that the proposed method achieves a 97.4% recognition accuracy, and all processes, including the generation of control commands for the myoelectric hand, are completed within 97 ms. Consequently, these results confirm that the proposed method is applicable to real-time EMG pattern recognition for multifunction myoelectric hand control.     

基于特征融合的海洋哺乳动物声音识别

为了提高海洋哺乳动物声音识别算法的识别率和鲁棒性,提出了一种将梅尔倒谱系数MFCC、线性倒谱系数LFCC和时域特征融合作为特征参数进行声音识别的方法。该方法通过融合不同倒谱系数以增强对不同频段的表征能力,通过融合时域特征来更全面地描述声音信息。声音样本通过基于海洋环境下的预处理、特征提取与融合后,用支持向量机进行分类识别。相对于传统算法只针对一种或几种哺乳动物进行识别,该方法在包含61种海洋哺乳动物声音的样本库中进行测试。测试结果显示该算法较传统的梅尔倒谱系数在识别率上提升了5.5%,且在海洋低信噪比环境下有更好的识别表现。     

Feature Extraction Based on Speech Attractors in the Reconstructed Phase Space for Automatic Speech Recognition Systems

In this paper, a feature extraction (FE) method is proposed that is comparable to the traditional FE methods used in automatic speech recognition systems. Unlike the conventional spectral‐based FE methods, the proposed method evaluates the similarities between an embedded speech signal and a set of predefined speech attractor models in the reconstructed phase space (RPS) domain. In the first step, a set of Gaussian mixture models is trained to represent the speech attractors in the RPS. Next, for a new input speech frame, a posterior‐probability‐based feature vector is evaluated, which represents the similarity between the embedded frame and the learned speech attractors. We conduct experiments for a speech recognition task utilizing a toolkit based on hidden Markov models, over FARSDAT, a well‐known Persian speech corpus. Through the proposed FE method, we gain 3.11% absolute phoneme error rate improvement in comparison to the baseline system, which exploits the mel‐frequency cepstral coefficient FE method.     

Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases

Current trends in clinical applications demand automation in electrocardiogram (ECG) signal processing and heart beat classification. This paper examines the design of an effective recognition method to diagnose heart diseases. The proposed method consists of three main modules: de-noising module, feature extraction module, and classifier module. In the de-noising module, multiscale principal component analysis (MSPCA) is used for noise reduction of the ECG signals. In the feature extraction module, autoregressive (AR) modeling is used for extracting features. In the classifier module, different classifiers are examined such as simple logistic, k-nearest neighbor, multilayer perceptron, radial basis function networks, and support vector machines. Different experiments are carried out using the MIT-BIH arrhythmia database to classify different ECG heart beats and the performance of the proposed method is evaluated in terms of several standard metrics. The experimental results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately in comparison to previous methods. The numerical results indicated that the proposed algorithm achieved 99.93 % of the classification accuracy using MSPCA de-noising and AR modeling.