As many acoustic signal processing methods, for example for source separation or noise canceling, operate in the magnitude spectrogram domain, the problem of reconstructing a perceptually good sounding signal from a modified magnitude spectrogram, and more generally to understand what makes a spectrogram consistent, is very important. In this article, we derive the constraints which a set of co...

The subject matter presented in this paper deals with application of the short time Fourier transform (STFT) in processing the acoustic emission (AE) pulses generated by six basic forms of partial discharges (PDs) that can occur in insulation systems of power appliances. The paper presents the time-frequency analysis results of the AE pulses generated by the following spark gaps modeling the fo...

The modification of magnitude spectrograms is at the core of many audio signal processing methods, from source separation to sound modification or noise canceling, and reconstructing a natural sounding signal in such situations is thus a very important issue. This article presents recent theoretical and experimental developments on the application to signal reconstruction from a modified magnit...

This paper is concerned with a synthesis study of the fast Fourier transform (FFT), the short-time Fourier transform (STFT), the Wigner distribution (WD) and the wavelet transform (WT) in analysing the phonocardiogram signal (PCG). It is shown that these transforms provide enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-f...

The frequency contents of the non-stationary signals vary with time. For proper characterization of such signals, a smart time-frequency representation is necessary. Classically, the STFT (short-time Fourier transform) is employed for this purpose. Its limitation is the fixed timefrequency resolution. To overcome this drawback an enhanced STFT version is devised. It is based on the signal drive...

Recently, a Monte Carlo approach was proposed for processing highly redundant continuous frames. In this paper, we present and analyze applications of new theory. The computational complexity the method relies on frame being so-called linear volume discretizable (LVD). LVD property means that number samples in coefficient space required by is proportional to resolution discrete signal. We show ...

Abstract This paper presents a study of the impact clicks, and murmurs on cardiac sound S1, S2, measure severity degree through synchronization between frequencies, using bispectral analysis. The algorithm is applied three groups Phonocardiogram (PCG) signal: group A represents PCG signals having morphology similar to that normal signal without click or murmur, B with (reduced murmur), C repres...

In this paper we extend our previous work on model variety analysis of a signal processing algorithm with respect to the class of all input signals that may po tentially arise in a given signal understanding appli cation This analysis has two related objectives The rst objective is to partition the set of all possible sig nals in the application domain into two sets according to whether each si...

Deep learning-based techniques for automatic dysarthric speech detection have recently attracted interest in the research community. State-of-the-art typically learn neurotypical and discriminative representations by processing time-frequency input such as magnitude spectrum of short-time Fourier transform (STFT). Although these are expected to leverage perceptual cues, STFT do not necessarily ...

The wayside acoustic defective bearing detector system (TADS) is located on both sides of the railway, so that signals recorded by microphone not only include sound from train bearings but also it other disturbance sources. heavy noise and multisource would badly reduce reliability accuracy detection result TADS. In order to extract useful information signal exactly efficiently, a novel denoisi...