Uncertainty propagation techniques achieve a more robust automatic speech recognition by modeling the information missing after speech enhancement in the short-time Fourier transform (STFT) domain in probabilistic form. This information is then propagated into the feature domain where recognition takes place and combined with observation uncertainty techniques like uncertainty decoding. In this...

Received Jul 8, 2017 Revised Nov 20, 2017 Accepted Dec 11, 2017 This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio denoising technique based on Short Time F...

We report on the development of a novel Bayesian estimator for speech enhancement, which is capable of modelling the time and frequency dependencies of speech. Central to the development of the estimator is a conditional prior that is derived from the Markov Random Field theory. The proposed prior is a conditional Gaussian prior that defines the distribution of the amplitude of a speech STFT sa...

Convolutive mixtures of images are common in photography of semi-reflections. They also occur in microscopy and tomography. Their formation process involves focusing on an object layer, over which defocused layers are superimposed. Blind source separation (BSS) of convolutive image mixtures by direct optimization of mutual information is very complex and suffers from local minima. Thus, we devi...

Wiener filtering is one of the most widely used methods in audio source separation. It is often applied on time-frequency representations of signals, such as the short-time Fourier transform (STFT), to exploit their short-term stationarity, but so far the design of the Wiener time-frequency mask did not take into account the necessity for the output spectrograms to be consistent, i.e., to corre...

The ratio of the short time Fourier transform (STFT) coe cients of signals received at two sensors can factor out the role of the power spectrum of emitting sources, under an assumption called disjoint orthogonality. Thus, it can reveal parameters speci c to the mixing scenario and serve as a basis for channel estimation techniques. In this paper we analyze and extend a source separation method...

In state-of-the-art single channel short-time Fourier transform (STFT) based speech enhancement algorithms only the amplitude of the noisy speech signal is improved, but its phase is left unchanged. It is commonly assumed that the noisy phase is the best estimate of the clean phase available. While using the noisy phase is indeed optimal under certain statistical assumptions, in this paper we s...

Nowadays, Radar systems have many applications and radar imaging is one of the most important of these applications. Inverse Synthetic Aperture Radar (ISAR) is used to form an image from moving targets. Conventional methods use Fourier transform to retrieve Doppler information. However, because of maneuvering of the target, the Doppler spectrum becomes time-varying and the image is blurred. Joi...

Sound textures are often noisy and chaotic. The processing of these sounds must be based on the statistics of its corresponding time-frequency representation. In order to transform sound textures with existing mechanisms, a statistical model based on the STFT representation is favored. In this article, the relation between statistics of a sound texture and its time-frequency representation is e...

Most state-of-the-art speech enhancement (SE) techniques prefer to enhance utterances in the frequency domain rather than in the time domain. However, the overlap-add (OLA) operation in the short-time Fourier transform (STFT) for speech signal processing possibly distorts the signal and limits the performance of the SE techniques. In this study, a novel SE method that integrates the discrete wa...