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In this paper end point detection, MFCC and signal subspace Algorithm is proposed for speech enhancement. end point detection, MFCC and signal subspace Algorithm is used to reduce external noise, improve the quality of signal, and reduce signal loss. The main problem in speech communication is the background noise. It is not useful to keep the communication if the noise of the subway, train, car,...
In order to cope with the multi-source localization in near-field reverberant environment, approximated kernel density estimator (KDE) algorithm is introduced to provide robust anti-reverberation performance and multi-stage (MS) is used to solve the spectrum aliasing of high frequency on account of wide spacing of microphone array. Then spatial likelihood function (SLF) is built to mix the pairwise...
In single channel speech enhancement using Wiener filters, the accurate estimation of speech and noise periodograms forming the noisy speech periodogram is really important since it highly affects the performance. In some methods a Gaussian Mixture Model (GMM) is used to model the Probability Density Function (PDF) of speech and noise periodograms and then a Maximum A-Posteriori criterion is applied...
The a priori signal-noise-ratio (SNR) estimator plays a very crucial role in the performance of a noise reduction system. The decision-directed (DD) approach, which is the most widely used technique for estimating the a priori SNR, suffers from one-frame delay bias when following the a posteriori SNR. Many modifications of the DD approach in the literature focus on accelerating the tracking speed...
This paper addresses the problem of single channel speech enhancement algorithm in non-stationary noise environment which is rather difficult compared to the stationary noise. We proposed a new speech enhancement algorithm based on compressive sensing. First, the noise average estimation and Wiener filter gain are calculated. Compressive sensing using GPSR technique is then incorporated by randomly...
Human voice or speech is the most powerful mode for the communication that exist today. The speech production model is commonly known as source filter theory where the source represents the airflow from lungs passing through human glottis and the filter represents the effect of the vocal tract and lip radiation. Glottal source conveys the useful information. In this paper three techniques are implemented...
This paper proposes a novel method for estimation of strength of excitation (SoE) from speech signal. Using lowpass filtering to remove the effect of relatively high frequency vocal tract characteristics, we estimate epoch locations. Using these epoch locations we estimate SoE. The database used for evaluation purpose is CMU-ARCTIC database consisting of electroglottograph (EGG) signals. In addition,...
Compressed Sensing (CS) allows to efficiently acquire and compress a signal within a single operation. However, reconstructing the original signal is typically expensive. Hence, being able to perform signal processing operations in the compressed domain is extremely important. In this paper we propose a new technique to perform classification tasks in the compressed domain. In order to perform compressive...
Background noise is a severe problem in speech related systems. In order to solve this problem, it is important to eliminate the noise from the noisy speech, which is called speech enhancement. Typical speech enhancement algorithms only operate on the short-time magnitude spectrum, while keeping the short-time phase spectrum unchanged for synthesis. Or only compensate the phase spectrum while keeping...
Extended Kalman filter frequency tracker converges to an optimal estimation with a very low computational complexity only if an exact knowledge about the initial state is available and the model is accurate otherwise it may diverge. On the other hand, particle filter performance is not so sensitive to the initial state estimation and results in a more robust estimation even if no prior knowledge about...
Objective measures of speech quality are attractive because they facilitate performance assessment of hearing aids (HAs) without the need for human listeners. Objective speech quality predictions are usually performed intrusively, wherein the “closeness” between the reference and HA output speech recordings is quantified. In this paper, we focus on nonintrusive estimation of HA speech quality based...
In general for any speech processing, represented speech signals are pre-processed for some features at front end and some estimation are performed at back end. Hidden Markov Model is exclusively used for modeling time-varying vector sequences due to its simplicity. It also provides high accuracy in non-stationary environment. In this paper, HTK (Hidden Markov model Tool-Kit) toolkit is used for compiling...
Direction of Arrival (DOA) estimation of signal deals in estimating the direction from where the signal is originating by joint processing the multi-channel signals captured by the array of sensors. This paper presents the comparison between various Direction of Arrival Estimation techniques for the speech signal. Short-Time Fourier Transform (STFT) is used as a tool to transform the time domain signal...
Depression is considered as a psychosomatic state associated with the soft biometric features. People suffering from depression always behave abnormal. Depression is a clinically proven disorder that can overwhelm a person and his ability to perform even a simple task. Soft biometric provides important information about a person without being enough for their verification because they lack uniqueness...
This paper presents low cost laboratories that aim to enhance the teaching of electrical and electronic engineering. The laboratories have been designed and developed based on the Raspberry Pi microprocessor system and are aimed at exposing students to the integration of software and hardware in electrical engineering in addition to ensuring that students appreciate the theoretical foundations of...
This paper presents an instantaneous pitch estimation method based on data adaptive time domain filtering and multivariate synchrosqueezing transform (SST). The filtering approach is implemented with bivariate empirical mode decomposition (bEMD) using white Gaussian noise (wGn) as the reference signal. The bEMD decomposes speech and wGn together into a finite set of intrinsic mode functions (IMFs)...
This study introduces a method that employs a wavelet zerotree-based shrinkage algorithm motion-adaptively-combined with optical flow estimation for video frame rate up-conversion. In the method, an optical flow estimation technique is used to predict and insert frames between existing ones, and then, the predicted frames are denoised by using a specific wavelet-based algorithm, where each pixel location...
This paper addresses the problem of speaker identification in noisy conditions. A two-step noise reduction algorithm based on soft mask and minimum mean square error short-time spectral amplitude estimator was proposed. It is used in the signal preprocessing stage for more robust speaker identification. The proposed algorithm was tested and compared with the existing noise reduction algorithms in...
In this paper, we propose a temporal modulation spectral resto-ration (TMSR) approach for robust feature extraction in automatic speech recognition. There were three main function blocks in TMSR. First, mean and variance normalization (CMVN) was applied to the original feature sequence. Second, the noise characteristic was estimated with an analysis of the normalized features. Third, a gain function...
Parkinson's disease has affected over 6.3 million people across the globe. It is estimated that by 2030, the number would rise to 9 million. Almost twenty percent of the people still remain undiagnosed. Parkinson's is the second most common neurodegenerative disease after Alzheimer's. It not only claims the lives of the patients suffering from it but also adversely impacts the lives of their loved...
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