Title: Speech Enhancement via Deep Spectrum Image Translation Network (2019)

Quality and intelligibility of speech signals are degraded under additive background noise which is a critical problem for hearing aid and cochlear implant users. Motivated to address this problem, we propose a novel speech enhancement approach using a deep spectrum image translation network. To this end, we suggest a new architecture, called VGG19-UNet, where a deep fully convolutional network known as VGG19 is embedded at the encoder part of an image-to-image translation network, i.e. U-Net. Moreover, we propose a perceptuallymodified version of the spectrum image that is represented in Mel frequency and power-law non-linearity amplitude domains, representing good approximations of human auditory perception model. By conducting experiments on a real challenge in speech enhancement, i.e. unseen noise environments, we show that the proposed approach outperforms other enhancement …

Title: Image to Image Translation based on Convolutional Neural Network Approach for Speech Declipping (2019)

Clipping, as a current nonlinear distortion, often occurs due to the limited dynamic range of audio recorders. It degrades the speech quality and intelligibility and adversely affects the performances of speech and speaker recognitions. In this paper, we focus on enhancement of clipped speech by using a fully convolutional neural network as U-Net. Motivated by the idea of image-to-image translation, we propose a declipping approach, namely U-Net declipper in which the magnitude spectrum images of clipped signals are translated to the corresponding images of clean ones. The experimental results show that the proposed approach outperforms other declipping methods in terms of both quality and intelligibility measures, especially in severe clipping cases. Moreover, the superior performance of the U-Net declipper over the well-known declipping methods is verified in additive Gaussian noise conditions.

Title: Sequential use of spectral models to reduce deletion and insertion errors in vowel detection (2018)

From both perspectives of speech production and speech perception, vowels as syllable nuclei can be considered as the most significant speech events. Detection of vowel events from a speech signal is usually performed by a two-step procedure. First, a temporal objective contour (TOC), as a time-varying measure of vowel similarity, is generated from the speech signal. Second, vowel landmarks, as the places of vowel events, are extracted by locating prominent peaks of the TOC. In this paper, by employing some spectral models in a sequential manner, we propose a new framework that directly addresses three possible errors in the vowel detection problem, namely vowel deletion, consonant insertion, and vowel insertion. The proposed framework consists of three main steps as follows. At the first step, two solutions are proposed to essentially reduce the initial vowel deletion error. The first solution is to use the …

Title: Vowel detection using a perceptually-enhanced spectrum matching conditioned to phonetic context and speaker identity (2017)

Vowel detection methods usually adopt a two-stage procedure for detecting vowel landmarks. First, a temporal objective contour (TOC), as a time-varying measure of vowel-likeness, is generated from the speech signal. Then, vowel landmarks are extracted by determining outstanding peaks of the TOC. By focusing on the TOC generation stage, this paper presents a new model based on some proposed components called matched filters (MFs). Extraction of the MFs and design of the MF-based model constitute our two main contributions. Motivated by the human auditory system, the MFs are extracted by applying a series of perceptually-based processing operations to the speech spectra of the voiced frames. Accordingly, any factor leading to the variation of the speech spectra will change the extracted MFs, too. So, it is necessary to condition the filters to the factors affecting their characteristics. Based on this fact …

Background estimation in kernel space (2011)

One problem in background estimation is the inherent change in the background such as waving tree branches, water surfaces, camera shakes, and the existence of moving objects in every image. In this paper, a new method for background estimation is proposed based on function approximation in kernel domain. For this purpose, Weighted Kernel-based Learning Algorithm (WKLA) is designed. WKLA includes a weighted type of kernel least mean square algorithm with ability to function approximation in the presence of noise. So, the proposed background estimation method includes two stages: firstly, a novel algorithm for outlier detection namely Fuzzy Outlier Detector (FOD) is applied. Then obtained results are fed to the WKLA. The proposed approach can handle scenes containing moving backgrounds, gradual illumination changes, camera vibrations, and non-empty backgrounds. The qualitative results and …

Title: Kernel least mean square features For HMM-based signal recognition (2010)

In this paper, an attempt is made to propose a new feature extraction method that is capable of capturing nonlinearities in signals. For this purpose, Kernel Least Mean Square KLMS (KLMS) method is used to extract features from signal and in order to evaluate it, Hidden Markov Model (HMM) is used to model extracted feature sequence and to recognize it from other models. In HMM, Gaussian Mixture Model is used. By introducing noise on signal, results showed that recognition rate in the same level of noise is good but in other SNR values it can degrade. It is also compared with Linear Predictive Coding (LPC). Results showed that in low noise level, the proposed feature extraction has better results but in high noise level LPC has better results.

Title: A novel approach in video scene background estimation (2010)

This paper presents a novel method for background estimation in a video sequence from the function estimation point of view. The proposed algorithm, called Kernel-based Background Learning (KBL), is designed based on kernel machine joint with learning schemes. In order to estimate background using KBL algorithm, we first interpret foreground samples as outliers relative to the background ones and so propose an Outlier Separator (OS). Then, the obtained results of OS algorithm are employed in the KBL method in order to train and estimate background in each pixel. Experimental results show the high accuracy and effectiveness of the proposed method in background estimation and foreground detection for the scenes including moving backgrounds, camera shakes, and non-empty backgrounds.