Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition is an open problem. Extracting features out of poor quality prints is the most challening problem faced in this area. This paper introduces a new approach for fingerprint enhancement based on Short Time Fourier Transform(STFT) Analysis. STFT is a well known technique to analyze non-stationa...

Reverberation is damaging to both the quality and the intelligibility of a speech signal. We propose a novel single-channel method of dereverberation based on a linear filter in the Short Time Fourier Transform domain. Each enhanced frame is constructed from a linear sum of nearby frames based on the channel impulse response. The results show that the method can resolve any reverberant signal w...

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The dependability of power electronics systems, such as three-phase inverters, is critical in a variety applications. Different types failures that occur an inverter circuit might affect system operation and raise the entire cost manufacturing process. As result, detecting identifying problems for devices industry. This study presents short-time Fourier transform (STFT) fault classification ide...

In this paper, a crack diagnosis framework is proposed that combines new signal-to-imaging technique and transfer learning-aided deep learning to automate the diagnostic process. The objective of convert one-dimensional (1D) acoustic emission (AE) signals from multiple sensors into two-dimensional (2D) image capture information under variable operating conditions. process, short-time Fourier tr...

We obtain estimates for the Lp-norm of short-time Fourier transform (STFT) functions in modulation spaces, providing information about concentration on a given subset R2, leading to deterministic guarantees perfect reconstruction using convex optimization methods. More precisely, we large sieve inequalities Donoho-Logan type, but instead localizing signals regions T×W time-frequency plane inter...

In this paper a fast method for the calculation of a linear time-frequency distribution based on the gammatone lter auditory model is introduced as a preprocessing step for the automated transcription of music and auditory source separation. Examples show that this method has a promising potential for the analysis of music pieces with a limited spectral overlap of the diierent signal components...

The ability to classify drones using radar signals is a problem of great interest. In this paper, we apply convolutional neural networks (CNNs) the Short-Time Fourier Transform (STFT) spectrograms simulated reflected from drones. vary in many ways that impact STFT spectrograms, including blade length and rotation rates. Some these physical parameters are captured Martin Mulgrew model which was ...