In mixtures of speech signals the energy content of the components of the mixture is important and determine the structure of the mixture. Energy contents of signals are better shown when time-frequency or time-scale planes are used. In this paper we present a comparison of wavelet transform (WT) and short time Fourier Transform (STFT) in spectral analysis of speech signals. We will show in wav...

This paper compares wavelet and STFT analysis for a speakerindependent stop classification task using the TIMIT database. In the designed experiment the HMM classifier had to assign each test token to one of the following stop classes [d,g,b,t,k,p,dx]. On 6332 stops the wavelet features obtained an overall accuracy of 86 % which corresponds to a 14 % relative error reduction compared to the STF...

In this paper we describe a frequency-domain framework for source identification, separation and manipulation in stereo music recordings. Based on a simplified model of the stereo mix, we describe how a similarity measure between the Short-Time Fourier Transforms (STFT) of the input signals is used to identify time-frequency regions occupied by each source based on the panning coefficient assig...

Heart murmurs are sounds made by rapid blood flow in the heart. Abnormal heart can be a sign of serious conditions such as arrhythmia and cardiovascular diseases. Therefore, murmur classification is crucial for early detection conditions. To this end, we study problem training selected convolutional neural network (CNN) models (such VGGNet ResNet) using various signal representations spectrogra...

Direction of arrival estimation LFM signal is an essential task in radar, sonar, acoustics and biomedical. In this paper, a short time Fourier transform multi-step knowledge aided iterative generalized minimum residual (STFT-MS-KAI-GMRES) approach presented to amend the angle measurement signal. A three stage algorithm proposed. First, process initiated with formulating for carrier frequency ch...

Spectrum sensing is a crucial technology for cognitive radio. The existing spectrum methods generally suffer from certain problems, such as insufficient signal feature representation, low efficiency, high sensibility to noise uncertainty, and drastic degradation in deep networks. In view of these challenges, we propose method based on short-time Fourier transform improved residual network (STFT...

We present an algorithm for reconstructing a time-domain signal from the magnitude of a short-time Fourier transform (STFT). In contrast to existing algorithms based on alternating projections, we offer a novel approach involving numerical root-finding combined with explicit smoothness assumptions. Our technique produces high-quality reconstructions that have lower signal-to-noise ratios when c...