Window Design for Signal-Dependent Spectrogram using Optimal Kernel Techniques
نویسندگان
چکیده
Time-frequency distributions (TFDs) have proven useful in a wide variety of nonstationary signal processing applications. While sophisticated optimal bilinear TFDs have been developed to extract the maximum possible timefrequency information from signals, certain applications dictate simpler linear, running-FFT processing techniques. In this paper, we propose a signal-dependent short-time Fourier transform I spectrogram that enjoys many of the advantages of optimal bilinear TFDs yet retains the simplicity and efficiency of running-FFT processing. In addition, we extend the optimal kernel design problem to linear spaces of signals.
منابع مشابه
Adaptive Segmentation with Optimal Window Length Scheme using Fractal Dimension and Wavelet Transform
In many signal processing applications, such as EEG analysis, the non-stationary signal is often required to be segmented into small epochs. This is accomplished by drawing the boundaries of signal at time instances where its statistical characteristics, such as amplitude and/or frequency, change. In the proposed method, the original signal is initially decomposed into signals with different fr...
متن کاملA Signal-Dependent Evolution Kernel for Cohen Class Time-Frequency Distributions
Cohen class time–frequency distributions serve as alternatives to the traditional spectrogram and are known for their ability to provide simultaneous resolution in time and frequency. They employ a kernel along with the signal’s Wigner distribution. Kernel design has witnessed significant attention. Very recently Costa and BoudreauxBartels have proposed a multiform tiltable exponential distribu...
متن کاملMultiple window spectrogram and time-frequency distributions
We extend the spectrum estimation method of Thomson to non-stationary signals by formulating a multiple window spectrogram. The traditional spectrogram can be represented as a member of Cohen’s class of time-frequency distributions (TFDs), where the smoothing kernel is the Wigner distribution of the signal temporal window. We show the unusual shape of the Cohen’s class smoothing kernels corresp...
متن کاملAn Adaptive Optimal - Kerneltime - Frequency
Time-frequency representations with xed windows or kernels gure prominently in many applications, but perform well only for limited classes of signals. Representations with signal-dependent kernels can overcome this limitation. However, while they often perform well, most existing schemes are block-oriented techniques unsuitable for on-line implementation or for tracking signal components with ...
متن کاملNon-Stationary Signal Segmentation and Separation from Joint Time-Frequency Plane
Multi-components sinusoidal engineering signals who are non-stationary signals were considered in this study since their separation and segmentations are of great interests in many engineering fields. In most cases, the segmentation of non-stationary or multi-component signals is conducted in time domain. In this paper, we explore the advantages of applying joint time-frequency (TF) distributio...
متن کامل