Providing single and multi-channel acoustical robustness to speaker identification systems

Real world conditions differ from ideal or laboratory conditions, causing mismatch between training and testing phases, and consequently, inducing performance degradation in automatic speaker recognition systems. Many strategies have been adopted to cope with acoustical degradation; in some applications of speaker identification systems a clean sample of speech, prior to the recognition stage, is needed. This has justified the use of procedures that may reduce the impact of acoustical noise on the desired signal, giving rise to techniques involved in the enhancement of noisy speech. A comparative performance analysis of single-channel (based in classical spectral subtraction and some derived alternatives), dual-channel (based in adaptive noise cancelling) and multi-channel (using microphone arrays) speech enhancement techniques, with different types of noise at different SNRs, as a pre-processing stage to an ergodic HMM-based speaker recognizer, is presented.

We investigate the use of Fourier plane nonlinear filtering for phase-encoded images. We investigate the performance of the nonlinear joint transform correlator and the nonlinearly transformed matched filter for phase-encoded images with different types of input noise. We use the peak-to-output-energy ratio, peak-to-sidelobe ratio, and discrimination ratio as the metrics for measuring the performances. We mathematically analyze the peak-to-output-energy ratio of the nonlinearly transformed matched filter for phase-encoded images with spatially nonoverlapping white noise. Computer simulations are provided to show the performance improvements of the nonlinear filtering techniques for the phase-encoded images. In comparison with linear filtering techniques, we find that the nonlinear filtering techniques substantially improve the performance metrics. From the computer-simulation results it can be seen that the nonlinear joint transform correlator performs better than the nonlinearly transformed matched filter in detecting phase-encoded targets in the presence of different types of noise, such as additive overlapping white noise, spatially nonoverlapping white background noise, spatially nonoverlapping colored background noise, and nontarget objects.

Electrocardiogram (ECG) signals are electrical signals generated corresponding to activity of heart. ECG signals are recorded and analysed to monitor heart condition. In initial raw form, ECG signals are contaminated with different types of noises. These noises may be electrode motion artefact noise, baseline wander noise and muscle noise also known as electromyogram (EMG) noise etc. In this paper, a descendent structure consists of adaptive filters is used to eliminate the three different types of noises (i.e., motion artefact noise, baseline wander noise and muscle noise). The two different adaptive filtering algorithms have been implemented; least mean square (LMS) and recursive least square (RLS) algorithm. The performance of these filters are compared on the basis of different fidelity parameters such as mean square error (MSE), normalised root mean squared error (NRMSE), signal-to-noise ratio (SNR), percentage root mean squared difference (PRD), and maximum error (ME) has been observed.

<span lang="EN-US">Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, ecommerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithmbased system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of a contextual bias or pre-centric understanding of a reference model with the validity of an unknown audio sample and any suspicious individual. Many researchers are continuing to develop automatic algorithms in signal processing and machine learning so that improving performance can effectively introduce the speaker’s identity, where the automatic system performs equally with the human audience. In this paper, I examine the literature about the identification of speakers by machines and humans, emphasizing the key technical speaker pattern emerging for the automatic technology in the last decade. I focus on many aspects of automatic speaker recognition (ASR) systems, including speaker-specific features, speaker models, standard assessment data sets, and performance metrics</span>

Oscillator noise is generally modeled by a power law spectral density. Thus it is possible to characterize different noise sources, each of them corresponding to a particular power law. The measurement of the contribution of these sources is necessary to know their origin and to remedy these causes in order to improve oscillator performance. Usually, an estimation of the different types of noise present in a signal is obtained by using a variance. However, the sensitivity of these variances differs for each type of noise and then limits this method. On the other hand, the use of several variances, each of them more sensitive to one type of noise, permits one to notably improve the measurement accuracy. The method suggested here uses as many different variances as there are types of noise to measure. The improvement of measurement accuracy of the noise coefficient is discussed in this paper.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this work, the effects of background noises on nonlinear dynamics of a modelled standing-wave thermoacoustic system with subcritical Hopf bifurcation behaviors are studied. These noises include (1) pressure-coupled (acoustic), (2) velocity-coupled (flow), and (3) external combustion noise. It is found that these three types of background noises play important, but different roles in changing the hysteresis width and stability boundary. In addition, the stochastic transition from stability to instability is investigated, as the noise intensity is varied. Two different stochastic P-bifurcations are identified. One is related to a craterlike probability density distribution. The other is associated with a probability density distribution characterized with two peaks and one trough. With each type of noise affecting the system's stochastic behaviors being evaluated, the effect of two different types of noises is then studied. It is shown that the combined noises (types 1 and 2) cannot only destabilize global stable thermoacoustic system, but also stabilize linearly unstable system. This depends strongly on the superimposition form of the two types of noises. In addition, when the thermoacoustic system is disturbed by the combined noise (types 3 and 1 or types 3 and 2), the transition process is dominated by the combustion noise.

Electrocardiogram (ECG) signal recording is a challenging task in the field of biomedical engineering. ECG is the cardiac recording of systematic electrical activity arising from the electro-physiological rhythm of the heart muscle. But, during processing, the ECG signal is contaminated with different types of noise in the medical environment. An immense task is the separation of the preferred signal from noises caused by artifacts like muscle noise, power line interference (PLI), baseline wandering (BW), and motion artifacts (MA). Hence, our paper focuses on 50 Hz PLI which is a major artifact/noise affecting the recorded ECG signal. This paper comprehensively reviews fundamental concepts of different denoising techniques. Some of the pioneers’ works are also concisely explained in the paper. Further, in this work, comparative analysis is carried out using notch filter, adaptive filter, discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for filtering 50 Hz PLI noise. A considerable improvement in signal-to-noise ratio (SNR) can be observed from the results when compared with SNR input and SNR output values. Performance comparisons of all the four techniques are also analyzed based on variations in noise frequency. The simulations were carried out in the environment of MATLAB 2019b®. This work epitomizes the significance of our quantitative evaluation, in which adaptive filters are found to perform better with respect to the SNR, whereas DWT performs better with assessment of mean square error (MSE).

In this paper, we present a new Speech Enhancement (SE) technique capable of running on a smartphone, as an assistive device for hearing aids (HAs). The developed method incorporates the coherence between the speech and noise signals to obtain a SE gain function which is used in conjunction with the gain function obtained by Spectral Subtraction using adaptive gain averaging. SE using coherence based gain function is found to suppress the background noise well, while inducing speech distortion. On the other hand, SE using Spectral Subtraction improves speech quality with tolerable speech distortion, but introduces background musical noise for certain noise types. The weighted fusion of the two gain functions strikes a balance between noise suppression and speech distortion. Also it allows the user to control the weighting factor based on the noisy environment and their comfort level of hearing. The developed method is computationally fast and operates in real-time. The proposed method was evaluated for machinery, babble, and car noise types, using both objective and subjective measures for both quality and intelligibility of the enhanced speech. The results show significant improvements in comparison with stand-alone Spectral Subtraction with weighted gain averaging SE methods.

Objective: to analyze speech perception in normally hearing adults when listening in silence and with different types of noise. Methods: 40 individuals of both sexes, aged 18 to 45 years, participated in the study. Speech perception was assessed with the Lists of Sentences in Portuguese test, without a competing noise and with speech-spectrum, babble, and cocktail party noise. A mixed-effects linear regression model and the 95% confidence interval were used. Results: the subjects’ performance was worse in the three types of noise than in silence. When comparing the types of noise, differences were found in all combinations (speech-spectrum X babble, speech-spectrum X cocktail party, and babble X cocktail party), with a worse performance in babble, noise, followed by cocktail party. Conclusion: all noises negatively influenced speech perception, with a worse performance in babble, followed by cocktail party and speech-spectrum.

In this paper, we consider a speech enhancement system for mobile phones using pair microphones. The locations of the two microphones are arranged to facilitate the derivation of speech absence probability. The results are substituted into an MMSE estimator in loudness domain for speech purification. Compared with the conventional algorithms [1, 2], which estimate the speech absence probability based on a single microphone, the proposed method does not require many empirical parameters and is relatively robust under different types of noises. The performance of this method is evaluated under noisy environments with music and car noises.

In this paper, some classical and modern spectral estimation algorithms are incorporated in the conventional spectral subtraction method for improving the performance of speech enhancement. There are three methods are proposed in this paper: the SSW method, the SSY method and the SSM method. Some typical environment noise signals are chosen from the common noise group. The noisy speech signals adopted in the experiments are the clean speech contaminated by the additive noise and the convolution noise at different signal-to-noise ratios respectively. The Itakura-Saito spectrum distance between the enhanced speech and the clean speech is served as the measurement of the quality of the speech enhancement system. Each type of the noisy speech signals will be enhanced by the conventional spectrum subtraction method and the three proposed methods respectively. Experimental results demonstrated the superior performance of the proposed methods over the conventional spectrum subtraction method. It is concluded that each of them is adept to manipulating different type of noises according to the corresponding characteristics.

In this research, we present an automatic speaker recognition system based on adaptive orthogonal transformations. To obtain the informative features with a minimum dimension from the input signals, we created an adaptive operator, which helped to identify the speaker’s voice in a fast and efficient manner. We test the efficiency and the performance of our method by comparing it with another approach, mel-frequency cepstral coefficients (MFCCs), which is widely used by researchers as their feature extraction method. The experimental results show the importance of creating the adaptive operator, which gives added value to the proposed approach. The performance of the system achieved 96.8% accuracy using Fourier transform as a compression method and 98.1% using Correlation as a compression method.

In this work, we propose ShallowDeepNet, a novel system architecture that includes a shallow and a deep neural network. The shallow neural network has the duty of data preprocessing and generating adversarial samples. The deep neural network has the duty of understanding data and information as well as detecting adversarial samples. The deep neural network gets its weights from transfer learning, adversarial training, and noise training. The system is examined on the biometric (fingerprint and iris) and the pharmaceutical data (pill image). According to the simulation results, the system is capable of improving the detection accuracy of the biometric data from 1.31% to 80.65% when the adversarial data is used and to 93.4% when the adversarial data as well as the noisy data are given to the network. The system performance on the pill image data is increased from 34.55% to 96.03% and then to 98.2%, respectively. Training on different types of noise can benefit us in detecting samples from unknown and unseen adversarial attacks. Meanwhile, the system training on the adversarial data as well as noisy data occurs only once. In fact, retraining the system may improve the performance further. Furthermore, training the system on new types of attacks and noise can help in enhancing the system performance.

Noise induced hearing loss (NIHL) as one of the major avoidable occupational related health issues has been studied for decades. To assess NIHL, the excitation pattern (EP) has been considered as one of the mechanisms to estimate the movements of the basilar membrane (BM) in the cochlea. In this study, two auditory filters, dual resonance nonlinear (DRNL) filter and rounded-exponential (ROEX) filter are applied to create two EPs, the velocity EP and the loudness EP respectively. Two noise hazard metrics are proposed based on two proposed EPs to evaluate hazardous levels caused by different types of noise. Moreover Gaussian noise and tone signals are simulated to evaluate performances of the proposed EPs and the noise metrics. The results show that both EPs can reflect the responses of the BM to different types of noise. For Gaussian noise there is a frequency shift between the velocity EP and the loudness EP. The results suggest that both EPs can be used for assessment of NIHL.

Diverse Non-Fundamental Information and Trading Complementarities