Under the condition of low signal-to-noise ratio, for the problem of insufficient speech feature
extraction and speech enhancement effect of the traditional neural network, this paper is …

This paper is a contribution to the old problem of representing a signal in the coordinates of
time and frequency. As the starting point, we abandon Gabor's complex extension and re …

In low signal-to-noise ratio (SNR) conditions, to address the problem of poor speech
enhancement effect of traditional neural networks, this paper combines Convolution …

We propose the discrete linear chirp transform (DLCT) for decomposing a non‐stationary
signal into intrinsic mode chirp functions. The decomposition of a signal into a finite number …

In many applications in signal processing, the discrete Fourier transform (DFT) plays a
significant role in analyzing characteristics of stationary signals in the frequency domain …

In this paper, we consider FM demodulation as an application of the decomposition of non-
stationary signals. Non-stationary signal decomposition can be done using either the …

Compressed sensing, as a novel framework, needs a small amount of characteristic datas to
reconstruct the original datas and breakthroughs the limitation of the Nyquist sampling law …

The problem of separating mixtures of speech signals has always been a heated topic in
speech processing. Multiple speech separation approaches have been proposed and a …

This work examines two main areas in model-based time-varying signal processing with
emphasis in speech processing applications. The first area concentrates on improving …

In this paper, we consider FM demodulation as an application of the decomposition of non–
stationary signals. Non–stationary signal decomposition can be done using either the …