Jacqueline Walker

Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential

The electromyographic (EMG) signal provides information about the performance of muscles and nerves. At any instant, the shape of the muscle signal, motor unit action potential (MUAP), is constant unless there is movement of the position of the electrode or biochemical changes in the muscle due to changes in contraction level. The rate of neuron pulses, whose exact times of occurrence are random in nature, is related to the time duration and force of a muscle contraction. The EMG signal can be modeled as the output signal of a filtered impulse process where the neuron firing pulses are assumed to be the input of a system whose transfer function is the motor unit action potential. Representing the neuron pulses as a point process with random times of occurrence, the higher order statistics based system reconstruction algorithm can be applied to the EMG signal to characterize the motor unit action potential. In this paper, we report results from applying a cepstrum of bispectrum based system reconstruction algorithm to real wired-EMG (wEMG) and surface-EMG (sEMG) signals to estimate the appearance of MUAPs in the Rectus Femoris and Vastus Lateralis muscles while the muscles are at rest and in six other contraction positions. It is observed that the appearance of MUAPs estimated from any EMG (wEMG or sEMG) signal clearly shows evidence of motor unit recruitment and crosstalk, if any, due to activity in neighboring muscles. It is also found that the shape of MUAPs remains the same on loading.

A review of glottal waveform analysis

Glottal inverse filtering is of potential use in a wide range of speech processing applications. As the process of voice production is, to a first order approximation, a source-filter process, then obtaining source and filter components provides for a flexible representation of the speech signal for use in processing applications. In certain applications the desire for accurate inverse filtering is more immediately obvious, e.g., in the assessment of laryngeal aspects of voice quality and for correlations between acoustics and vocal fold dynamics, the resonances of the vocal tract should firstly be removed. Similarly, for assessment of vocal performance, trained singers may wish to obtain quantitative data or feedback regarding their voice at the level of the larynx.

A New Spike Detection Algorithm for Extracellular Neural Recordings

Signals from extracellular electrodes in neural systems record voltages resulting from activity in many neurons. Detecting action potentials (spikes) in a small number of specific (target) neurons is difficult because many neurons, both near and more distant, contribute to the signal at the electrode. We consider some nearby neurons as target neurons (providing a signal) and all the other contributions to the signal as noise. A new algorithm for spike detection has been developed: this applies a cepstrum of bispectrum (CoB) estimated inverse filter to provide blind equalization. This technique is based on higher order statistics, and seeks to find a sequence of event times or delta sequence. We show that the CoB-based technique can achieve a 98% hit rate on an extracellular signal containing three spike trains at up to 0 dB SNR. Threshold setting for this technique is discussed, and we show the application of the technique to some real signals. We compare performance with four established techniques and report that the CoB-based algorithm performs best.

Characterization of a Flip-Flop Metastability Measurement Method

We characterize a proposed metastability measurement system in which asynchronous data input and sampling clock frequencies trigger metastability. We develop an equation describing the time interval between data and clock inputs for practical frequencies and show that it takes on discrete values in the absence of jitter and that the presence of jitter perturbs these values. Finally, we present experimental results supporting our characterization

Advanced methods for glottal wave extraction

Glottal inverse filtering is a technique used to derive the glottal waveform during voiced speech. Closed phase inverse filtering (CPIF) is a common approach for achieving this goal. During the closed phase there is no input to the vocal tract and hence the impulse response of the vocal tract can be determined through linear prediction. However, a number of problems are known to exist with the CPIF approach. This review paper briefly details the CPIF technique and highlights certain associated theoretical and methodological problems. An overview is then given of advanced methods for inverse filtering: model based, adaptive iterative, higher order statistics and cepstral approaches are examined. The advantages and disadvantages of these methods are highlighted. Outstanding issues and suggestions for further work are outlined.

Musical instrument identification using Principal Component Analysis and Multi-Layered Perceptrons

This study aims to create an automatic musical instrument classifier by extracting audio features from real sample sounds. These features are reduced using Principal Component Analysis and the resultant data is used to train a Multi-Layered Perceptron. We found that the RMS temporal envelope and the evolution of the centroid gave the most interesting results of the features studied. These results were found to be competitive whether the scope of the data was across one octave or across the range of each instrument.

Cepstrum of bispectrum-A new approach to blind system reconstruction

In this paper, an improved approach to blind deconvolution of LTI systems incorporating phase unwrapping is presented. The method can recover a noise-free estimate of the logarithm of the system transfer function which enables reconstruction of the system. The algorithm is fast due to simple computation and accurate as it includes phase unwrapping. The proposed method is compared via simulation with other methods, selected as representative of both bispectrum- and bicepstrum-based techniques. In general, it performs as well as or much better than the other methods considered. The proposed method is also shown to perform well under low signal-to-noise ratios.

Determining parameters to minimize jitter generation in the SRTS method

The synchronous residual time stamp (SRTS) method has been recommended by the ITU-T as one approach to the transfer of timing information for continuous bit rate (CBR) services being carried by the asynchronous transfer mode (ATM) adaptation layer 1 (AAL1) of the broadband integrated services digital network (B-ISDN). We give an overview of the SRTS method including a description of service clock recovery at the destination. On the basis of analytical expressions derived, we show that even in an ideal SRTS system, where all clocks are jitter-free, the SRTS method inherently generates jitter. We also show that this jitter is equivalent to the so-called waiting time jitter that is found in conventional timing justification techniques. In other systems where waiting time jitter is generated, parameters are chosen in order to minimize the amount of waiting time jitter produced and in which part of the spectrum it is located. We show that for the SRTS method, the choice of parameters to minimize waiting time jitter is quite restricted. For higher service clock frequencies and clocks with large tolerances, it may be impossible to choose parameters to influence the characteristics of the waiting time jitter produced.

On the scalability of particle swarm optimisation

Particle swarm has proven to be competitive to other evolutionary algorithms in the field of optimization, and in many cases enables a faster convergence to the ideal solution. However, like any optimization algorithm it seems to have difficulties handling optimization problems of high dimension. Here we first show that dimensionality is really a problem for the classical particle swarm algorithms. We then show that increasing the swarm size can be necessary to handle problem of high dimensions but is not enough. We also show that the issue of scalability occurs more quickly on some functions.

Genetic programming for musical sound analysis

This study uses Genetic Programming (GP) in developing a classifier to distinguish between five musical instruments. Using only simple arithmetic and boolean operators with 95 features as terminals, a program is developed that can classify 300 unseen samples with an accuracy of 94%. The experiment is then run again using only 14 of the most often chosen features. Limiting the features in this way raised the best classification to 94.3% and the average accuracy from 68.2% to 75.67%. This demonstrates that not only can GP be used to create a classifier but it can be used to determine the best features to choose for accurate musical instrument classification, giving an insight into timbre.