This article presents the theoretical considerations that justify the choice of specific time-frequency transforms for processing nonstationary myoelectric signals as a method of studying fatigue prior to the failure point. It shows some preliminary results obtained by applying these techniques to computer-synthesized realizations of stochastic processes, as well as to real signals detected during different types of dynamic contractions of healthy human volunteers. Five different time-frequency transforms were applied in this study (the Wigner-Ville, the smoothed Wigner-Ville, the Cone kernel, the reduced interference, and the Choi-Williams), but for the sake of brevity, this article reports only the results obtained by applying the Choi-Williams transform, because the authors found it to be the most suitable for processing these specific signals.

Analysis of myoelectric signals recorded during dynamic contractions

A general approach for obtaining joint representations for arbitrary physical quantities is presented. The characteristic function operator method of Moyal (1949) and Ville (1960) and generalized by Cohen (1966, 1976) and Scully and Cohen (1987) for the time-frequency case is developed for arbitrary variables.

A general approach for obtaining joint representations in signal analysis. I. Characteristic function operator method

The hyperbolic kernel and hyperbolic wavelet were shown to be useful and effective in time-frequency signal processing (Le et al., 2001 and 2003). We introduce the time-frequency wavelet power spectrum as a useful technique to study signal characteristics in which the hyperbolic and sym3 wavelets are employed. The hyperbolic wavelet power spectrum technique is employed for signals including electrocardiogram (ECG), sinusoidal, transient exponential, music, and speech. Comparisons between the Fourier power spectrum technique and hyperbolic wavelet power spectrum technique are also made.

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Hyperbolic wavelet power spectra of nonstationary signals

In this chapter, the accuracy of the analysis of rapidly varying formants using spectrogram and Linear Prediction is assessed. Analysis of various dynamic signals shows that, when a long analysis window, like 25 ms, is used, the quality of the representation may be impoverished. Obvious unwanted effects are staircaselike formant tracks, flattening-off of formants close to voicing onset, and bending of the formant towards a strong energy concentration in the release burst. The parameters that have the largest influence on the quality of the representation are the length of the analysis window, the transition rate of the formant, the fundamental frequency, and the position and energy of the release burst. It is shown that the most accurate analysis using a quasi-stationary method is made when windows are positioned pitch-synchronously. Finally, a quantitative analysis of the influence of the mentioned parameters provides evidence that no deviations due to the quasi-stationarity assumption occur when the effective length of the analysis window is not larger than the pitch period. The wideband spectrogram is expected to be a reliable speech-analysis tool because it meets this condition for fundamental frequencies up to 370 Hz.

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Papers

Signal analysis with cone kernel time-frequency distributions and their application to speech