Abstract
The short‐time Fourier transform (STFT) is a classical tool, used for characterizing the time varying signals. The limitation of the STFT is its fixed time‐frequency resolution. Thus, an enhanced version of the STFT, which is based on the cross‐level sampling, is devised. It can adapt the sampling frequency and the window function length by following the input signal local characteristics. Therefore, it provides an adaptive resolution time‐frequency representation of the input signal. The computational complexity of the proposed STFT is deduced and compared to the classical one. The results show a significant gain of the computational efficiency and hence of the processing power.
Highlights
Most of the real-life signals like speech, Doppler, seismic, and biomedical signals are time varying in nature
The limitation with the short-time Fourier transform (STFT) is that it provides a fixed resolution time-frequency representation of the input signal. This fixed resolution is the reason for the creation of the multiresolution analysis (MRA) techniques [3,4,5], which provide a good frequency but a poor time resolution for the low-frequency events and a good time but apoor frequency resolution for the high-frequency events
The STFT of a sampled signal xn is determined by computing the discrete Fourier transform (DFT) of an N samples segment centred on τ, which describes the spectral contents of xn around the instant τ
Summary
Most of the real-life signals like speech, Doppler, seismic, and biomedical signals are time varying in nature. The limitation with the STFT is that it provides a fixed resolution time-frequency representation of the input signal This fixed resolution is the reason for the creation of the multiresolution analysis (MRA) techniques [3,4,5], which provide a good frequency but a poor time resolution for the low-frequency events and a good time but apoor frequency resolution for the high-frequency events. This type of analysis is well suited for most of the real-life signals [3]. An efficient solution is proposed by smartly combining the features of both uniform and nonuniform signal processing tools
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