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It is essential to enhance the speech signal's clarity and quality in order to maintain the message's content. By boosting the noisy voice signal, the speech signal quality can be raised. Two techniques are presented in this study to significantly minimize the additive background noise. In order to minimize non-stationary additive noise concerning the speech signal, the first approach employs modified multiband spectral subtraction. With this technique, spectral subtraction is carried out based on the signal to noise ratio (SNR) values in various noisy speech frames. When the noisy signal and noise signal are somewhat correlated, a second method is used to minimize the cross spectral components. These techniques are used to get over the drawbacks of the fundamental spectrum subtraction method. To improve the noisy speech signal, both techniques are combined. [ABSTRACT FROM AUTHOR]

Speech is a signal produced by humans to interact and communicate. Different information is gained from speech signals, such as the language being spoken, emotion, gender, speaker identification, and other information. Speech signals are exposed to different noises, which can be generated at the beginning of the speech or during the transmission. Due to this problem, noise reduction processes are an interesting field in different communication application systems that cultivate the intelligibility and quality of speech signals. It refers to removing or reducing the background noise in order to obtain an improved quality of the original speech signal without distorting the original (clean) signal. This paper reviews the state-of-the-art research, reviewing different speech enhancement filters and algorithms and comparing their performance to reach a conclusion about which is the best filter or the most effective one based on the kind of noise that was used and the most difficult noise to remove from the signal. [ABSTRACT FROM AUTHOR]

Environmental Sound Classification (ESC), also referred as Sound Event Classification, is an essential part of many speech processing applications in terms of of separating background audio from original signal. By the recent developments in deep learning area, studies related to the ESC area have also been improved significiantly by the researchers. Because the nature of digital sound signals, the ESC was mostly developed using manually extracted one dimensional (1D) so far. In this paper, a novel ESC pipeline which uses spectral subtraction denoising as a preliminary stage was proposed based on deep learning architectures. The well-known deep learning architectures which are GoogLeNet, AlexNet, ShuffleNet, SqueezeNet and ResNet-18 were run over ESC problem by using ESC-10 benchmark dataset. Log-mel spectrogram images were preferred as feature matrices for mentioned networks. The results showed that the proposed SS-ESC model achieved the best results and outperformed many state-of-the-art methods with a test accuracy of 99.17% for the ESC-10 by the help of the AlexNet. These findings significiantly proved that the spectral subtraction denoising can contribute to the environmental sound classification problem in leveraging classification accuracy when it is used as a preliminary stage. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time–domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R2) for the linear fit of the RMS to the MRR. [ABSTRACT FROM AUTHOR]

Traffic safety in railway transport requires regular inspection of the rail condition to identify and timely eliminate defects. Eddy current flaw detection is one of the popular methods of non-destructive rail testing. The data (defectograms) obtained from eddy current flaw detectors during rail testing are produced in large volume and therefore need in efficient automatic analysis. The analysis means the process of detecting on defectograms the presence of defective areas and identification of structural elements of the rail track, taking into account noise and possible interferences of various natures. The threshold noise level is found to isolate signals from defects and structural elements. Its value can be distorted by electromagnetic influences superimposed on the signals, which have pronounced low frequency and periodicity. This interferences raises the threshold noise level, complicating to detect useful signals. In this regard, there is a need to suppress the effects of the described type. Therefore, there is a need for described interferences reduction. In this paper spectral subtraction was used as a method for interference reduction on eddy current defectograms. The interference function was defined as the sum of the low-frequency harmonics of the Discrete Fourier Transform of the original signal. Then the interference-free signal can be found by subtracting of low-frequency range harmonics. The right boundary of this range was called the threshold harmonic frequency. It was found minimizing the distance of the signal's autocorrelation function and expected autocorrelation. For it two kinds were proposed: the noise autocorrelation and the reference autocorrelation. Both approaches allow to determine the threshold harmonic frequency so that periodic interferences will be best reduced. The based on the Gaussian noise autocorrelation method is somewhat universal for eddy current defectograms. Whereas the based on reference autocorrelation method depends on specific data and recording equipment. For the eddy current defectogram data under consideration, the most suitable threshold harmonic frequency was found. The described approaches to periodic low-frequency interference reduction, in addition to eddy current testing, can be successfully used for other areas.

Several speech enhancement techniques like Spectral Subtraction, MMSE, Log-MMSE, $ \rm beta $ β -order MMSE, adaptive $ \rm beta $ β -order MMSE and compressive sensing methods are developed worldwide. Scientists, engineers and researchers have implemented, evaluated and tested all methods individually with the different speech corpus. However, we found few articles on comparative studies of various speech enhancement techniques. In the present paper, several speech enhancement techniques have been studied, and their performance in terms of speech quality measures is compared objectively and subjectively. The results have been evaluated not only through speech quality measures but also in terms of waveform and spectrogram for speech enhancement applications. For this, MATLAB is used for the simulation of all methods. After getting the enhanced speech signals, we evaluated their enhanced speech signals of the methods. Results in terms of objective evaluation parameters indicated that the adaptive $ \rm beta $ β -order MMSE-based method produces good-quality speech signals compared to the other methods. Also, we evaluated their enhanced speech signals using a listening test, i.e. subjective evaluation. In the subjective quality test through mean opinion score (using the listening test), the performance of the adaptive $ \rm beta $ β -order MMSE method and GOMP are equal. In the case of waveform and spectrogram, the visualisation of enhanced speech signal obtained from GOMP-based compressive algorithms is very close to clean speech signal. [ABSTRACT FROM AUTHOR]

Introduction: As an important part of ship manufacturing, parts are of great significance in the calculation of its reliability level. Methods: To achieve rapid damage detection of ship parts, amethod for measuring the reliability level of ship casting and forging parts based on spectral subtraction and vibration-acoustic principles was proposed. This method improves the spectral subtraction method by adding a percussion vibration signal and time-frequency analysis, and uses the principle of resonance acoustics to complete the construction of the test platform to obtain the natural frequency of the part and achieve nondestructive testing of the part. Results: The results show that using the Fabric data set as the task data set for experiments, the accuracy of the research method is 98.54%; the uncertainty is 5.58; the sensitivity detection is 0.26 µm. In the comparison of the spectrogram of the sound signal after noise reduction, this method has fewer yellow spots remaining on the spectrogram of the noise reduction sound signal, and almost no excess noise remains. In the comparison of modal simulation data of ship parts, the maximum relative error between the simulation data obtained by this method and the natural frequency value obtained from the experimental data is 2.3%, and there is no value exceeding 2.5%, so the error is small. Discussion: The above results show that this method can obtain more accurate natural frequencies of parts, can effectively calculate the reliability level of ship casting and forging parts, and provides a new method reference for the safe operation of ships. [ABSTRACT FROM AUTHOR]

The additive noise in the condition monitoring system using fiber Bragg grating (FBG) sensors, including white Gaussian noise and multifrequency interference, has a significantly negative influence on the fault diagnosis of rotating machinery. Spectral subtraction (SS) is an effective method for handling white Gaussian noise. However, the SS method exhibits poor performance in eliminating multifrequency interference because estimating the noise spectrum accurately is difficult, and it significantly weakens the useful information components in measured signals. In this study, an improved spectral subtraction (ISS) method is proposed to enhance its denoising performance. In the ISS method, a reference noise signal measured by the same sensing system without working loads is considered the estimated noise, the same sliding window is used to divide the power spectrums of the measured and reference noise signals into multiple frequency bands, and the formula of spectral subtraction in the standard SS method is modified. A simulation analysis and an experiment are executed by using simulated signals and establishing a vibration test rig based on the FBG sensor, respectively. The statistical results demonstrate the effectiveness and feasibility of the ISS method in simultaneously eliminating white Gaussian noise and multifrequency interference while well maintaining the useful information components. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time–domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R2) for the linear fit of the RMS to the MRR.

Recent years have seen intensive research and widespread application of 6G wireless transmission technology, providing a technical bridge for the application of speech network analysis to spoken English teaching. Thus, this paper suggests a method for improving 6G technology. This paper primarily focuses on the following areas: First, to prevent overfitting, the dropout technique under 6G is added to the model, and the speech feature extracted from the data by the coding network is directly utilized. We then utilize the feature for either reconstruction or classification purposes. Initially, we perform unsupervised reconstruction using unlabeled data, and once we obtain the network parameters, we use the labeled data for classification. The next step involves feeding both data simultaneously into the model to calculate the error. Finally, we use gradient descent to minimize the error and optimize the parameters until the model converges. This model can convert sound-related symbols or information into visual symbols, and it can also convert speech into text. Comparative analysis shows that the improved method in this paper is far better than other methods in terms of noise reduction, and the signal-to-noise ratio of this paper is 3.76 dB higher than that of the LMS algorithm and 1.36 dB higher than that of the spectral subtraction method, which can stimulate the students’ interest in learning spoken English and increase the student’s interest in learning by 13.64% as a whole. It can be summarized through the comparison that this paper’s improved method increases the clarity of speech network analysis, which helps students teach spoken English.

In this paper, we investigate a psychoacoustic model-driven spectral subtraction framework for enhancement of noisy speech. In the proposed framework, the noisy speech spectrum is separated into six distinct and unevenly frequency-spaced subbands as per the psychoacoustic model of the human hearing system, and spectral over-subtraction is applied independently in each subband. The noise in each subband is estimated using an adaptive noise estimator that does not require a speech pause tracker. To compute and update the noise, the noisy speech power is adaptively smoothed using a smoothing factor controlled by a posterior SNR. The performance of the proposed framework is evaluated using SNR, segmental SNR (SegSNR), and PESQ scores for a variety of non-stationary and stationary noise environments at varying SNR levels. The experimental results show that the proposed framework outperforms various up-to-date speech enhancement technologies on three extensively used objective metrics assessments and speech spectrograms. [ABSTRACT FROM AUTHOR]

There is a void in the knowledge of the acidification status of Eastern Canada's coastal waters. This knowledge is crucial to evaluating the threats posed to marine life, particularly oyster farming, a flagship of New Brunswick seafood production. In this study, we measured the temporal variability of pH and related environmental parameters in three bays of Northeastern New Brunswick. We also evaluated the potential impact of the observed pH levels on the Eastern oyster (Crassostrea virginica Gmelin, 1791), based on the available literature on this species' sensitivity to acidification. We investigated the presence of inherent cycles of pH with the Fourier transform and the spectral filtering technique. Our results show that pH is highly variable in the studied area, with values ranging from 7.31 to 8.90. A seasonal effect was apparent, as the pH fluctuations were set at the lowest level in winter when the cover of ice and snow on the bay was present. The spectral analysis revealed a clear semidiurnal tidal pattern of pH, this variable being inversely related to the water level in summer and directly related to it in winter. The spectral subtraction of all the tidal components allowed the detection of a circadian rhythm that was not in pace with the alternation between day and night but rather slowly drifted so that the pH troughs occurred at night during the full moon period. Short periodicities of circa 8 and 6 h also existed in two of the three bays. Based on current knowledge of C. virginica's sensitivity to acidification, this species' recruitment, growth, and survival are unlikely to be impacted by the present pH levels in the studied area. However, further acidification might overcome the resilience of C. virginica, especially that of the larvae that are produced during the winter in commercial hatcheries. [ABSTRACT FROM AUTHOR]

Floor vibrations for event localization has gained attention recently for its human-related applications such as footstep tracking. However, noise can corrupt signals, reduce signal-to-noise ratios (SNR), and lead to imprecise estimations of the event's amplitude and force. Techniques to remove noise have been developed such as bandpass filters, which eliminate noise without regard to overlapping event frequency components. These methods can corrupt the signal, removing important information about the event. The authors propose adapting a common speech processing technique, called spectral subtraction using half wave rectification, to remove only the noise's contribution. The Event Reconstructing Adaptive Spectral Evaluation (ERASE) approach is compared to unfiltered and Butterworth-filtered data in impact localization and force estimation through the Force Estimation and Event Localization (FEEL) Algorithm. A total of 810 impacts from ball drops of five different heights and impulse hammers across eighteen locations were utilized for testing. Signals were corrupted by noise from different sources. ERASE demonstrated 93.9% average impact localization accuracy and -2.40% ± 1.85% force magnitude error on a 99% confidence interval, improving the SNR verse the other filtering techniques. [ABSTRACT FROM AUTHOR]

In this paper, the architecture of Dynamic Quantile Tracking (DQT) for estimating the noise signal has been proposed for Spartan-6 DSP Field Programmable Gate Array (FPGA) device. It is simulated on MATLAB/Simulink environment using Xilinx System Generator (XSG) blocks for speech enhancement application and implemented on Spartan-6 SC6SLX45 FPGA board in real-time mode. The hardware resources for the proposed architecture were obtained in terms of Flip Flops, Logic Slices, LUTs, Bonded IOBs, DSP48A1s and RAMB18E1s. The performance of the proposed architecture has been evaluated and analyzed with the variation of different speech-to-noise ratio levels and varying types of noises. Also, their results are compared with other methods like cascaded median and modified cascaded median methods. All results are evaluated and measured in teams of PESQ score with variations of SNR level and various noises and also presented in waveforms and spectrogram. In terms of PESQ score, it is observed that the enhanced speech for the proposed model is a good quality speech signal compared to other method's enhanced speech and noisy speech signals. In terms of hardware, it is observed that based on the utilization of resources, the proposed model acquires only 30% of the resources of the Spartan-6 SC6SLX45 FPGA board during real-time processing. [ABSTRACT FROM AUTHOR]

Dysarthric speech is the noisy or source distortion speech. Reasonable speech enhancement is required to obtain higher communication quality for non-stationary noises. Owing to complexities in speech rate of dysarthric persons, understanding their speech is more critical and complex task. The generic recognition systems do not perform well in speech recognition. Hence, this paper proposes a Fractional Competitive Crow Search Algorithm-based Speech Enhancement Generative Adversarial Network (FCCSA-SEGAN) for enhancing the speech signal. Initially, at the pre-processing stage, the noise from the speech signal is removed using spectral subtraction method. Then, pre-processed signal is fed to speech enhancement, where signal quality is enhanced by the Speech Enhancement Generative Adversarial Network (SEGAN), which is trained by the developed FCCA. By the incorporation of Fractional Calculus (FC) and Competitive Crow Search Algorithm (CSSA), proposed FCCA is obtained, in which CSSA is hybridization of Crow Search Algorithm (CSA) and Competitive Swarm Optimizer (CSO). After that, the features, such as Multiple Kernel Weighted Mel Frequency Cepstral Coefficient (MKMFCC), Linear Prediction Cepstral Coefficient (LPCC), spectral flux, spectral crest, spectral centroid, and pitch chroma are extracted. Moreover, to increase the dimensionality of signal samples, noises are added to the original signal through data augmentation phase. Finally, using Competitive Crow Search Algorithm-based Hierarchical Attention Network (CCSA-based HAN), speech recognition process is done. In addition, the performance of the proposed method is evaluated using the UA speech database and the accuracy, sensitivity, and specificity of 0.930, 0.933, and 0.934 are obtained by the proposed method. By the proposed speech enhancement approach, higher Perceptual Evaluation of Speech Quality (PESQ) and lower Root Mean Square Error (RMSE) of 3.14, and 0.022 are attained. [ABSTRACT FROM AUTHOR]

In an uncontrolled scenario like hospitals, medical research centres, laboratories, forensics, the voice recordings are normally affected with babble noise. Most of the speech enhancement (SE) methods are limited by the colour noise due to non-consideration of phase aspect. Hence, the objective of the work is to depict a more efficient and compact SE method through the comparative study of conventional SE methods taking into account of both continuous speech and sustained vowels. This approach is supported with supervised voice activity detection (VAD). The study includes an experimental investigation of time and transform-based seven conventional methods. This approach consists of pre-processing, SE evaluation block and supervised VAD section. The speech segments are efficiently detected by VAD using bio-inspired Mel frequency cepstral coefficients and nonlinear features with support vector machine. This supervised VAD helps in reliable estimation of SNR-based parameters. The performance of each of the methods is evaluated with continuous NOIZEUS corpus at varying signal-to-noise ratio (SNR) levels, laryngeal pathology-based data from Saarbruecken voice database and voice disorder data collected from cancer hospitals. Experimental results show that the performance of the Wiener filtering and geometrical-based SE methods is found to be better comparatively, as far as continuous and sustained vowels are concerned with babble noise. The paper also reveals the significant involvement of nonlinear features in VAD, as the nonlinear dynamical methods are capable of analysing irregular behaviours of vocal cords. [ABSTRACT FROM AUTHOR]

The additive noise in the condition monitoring system using fiber Bragg grating (FBG) sensors, including white Gaussian noise and multifrequency interference, has a significantly negative influence on the fault diagnosis of rotating machinery. Spectral subtraction (SS) is an effective method for handling white Gaussian noise. However, the SS method exhibits poor performance in eliminating multifrequency interference because estimating the noise spectrum accurately is difficult, and it significantly weakens the useful information components in measured signals. In this study, an improved spectral subtraction (ISS) method is proposed to enhance its denoising performance. In the ISS method, a reference noise signal measured by the same sensing system without working loads is considered the estimated noise, the same sliding window is used to divide the power spectrums of the measured and reference noise signals into multiple frequency bands, and the formula of spectral subtraction in the standard SS method is modified. A simulation analysis and an experiment are executed by using simulated signals and establishing a vibration test rig based on the FBG sensor, respectively. The statistical results demonstrate the effectiveness and feasibility of the ISS method in simultaneously eliminating white Gaussian noise and multifrequency interference while well maintaining the useful information components.

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to many outliers in the pitch period detection of noisy speech signal, an improved pitch period detection method is proposed. Firstly, combining spectral subtraction and wavelet transform for noise reduction, we first windowed and framed the useful speech signal sequence obtained after noise reduction for signal endpoint and vowel subject detection. Then, in the second windowing and framing process, the multi window spectrum estimation method was introduced to further reduce the influence of some formants and residual noise on pitch detection. Finally, the improved main body extension method was used for pitch detection. The experimental simulation results show that compared with the traditional pitch detection method, the sample rate of the segment collected by the improved method is 87%, which is at least 5% higher than that of the traditional one. The improved method has higher accuracy of pitch detection.

Speech enables easy human-to-human communication as well as human-to-machine interaction. However, the quality of speech degrades due to background noise in the environment, such as drone noise embedded in speech during search and rescue operations. Similarly, helicopter noise, airplane noise, and station noise reduce the quality of speech. Speech enhancement algorithms reduce background noise, resulting in a crystal clear and noise-free conversation. For many applications, it is also necessary to process these noisy speech signals at the edge node level. Thus, we propose implicit Wiener filter-based algorithm for speech enhancement using edge computing system. In the proposed algorithm, a first order recursive equation is used to estimate the noise. The performance of the proposed algorithm is evaluated for two speech utterances, one uttered by a male speaker and the other by a female speaker. Both utterances are degraded by different types of non-stationary noises such as exhibition, station, drone, helicopter, airplane, and white Gaussian stationary noise with different signal-to-noise ratios. Further, we compare the performance of the proposed speech enhancement algorithm with the conventional spectral subtraction algorithm. Performance evaluations using objective speech quality measures demonstrate that the proposed speech enhancement algorithm outperforms the spectral subtraction algorithm in estimating the clean speech from the noisy speech. Finally, we implement the proposed speech enhancement algorithm, in addition to the spectral subtraction algorithm, on the Raspberry Pi 4 Model B, which is a low power edge computing device. [ABSTRACT FROM AUTHOR]

Speech processing plays a vital role in current speech communication applications. The major objective of digital speech is transmission of messages among human and computer systems. A Text-to-speech synthesizer is utilized for these transmission of speech. Many significant works are carried out in the previous speech synthesis framework but still having issues in quality speech generation. This work presents an effective text to speech synthesis system using minimum phase harmonic sinusoidal modeling in Punjabi language. To develop the presented system, initially input phoneme speech is converted into an overlapping frames. Then, spectral subtraction technique is utilized to eliminate the noise of overlapped frames for speech enhancement. Subsequently, spectral speech parameters such as Mel frequency cepstral coefficients (MFCC), excitation parmeters such as fundamental frequency, energy and their first order(delta) and second order (delta-delta) time derivatives are extracted. After extracting all the parameters, the speech signal is synthesized from the extracted speech parameters using minimum phase harmonic sinusoidal model. Here, this synthesis model is dependent on the extracted speech parameters and also the amplitudes, phases of sine waves for the production of synthetic speech.The presented speech waveform reconstruction method to speeh synthesis process is implemented in PYTHON platform. The experimental outcomes of the presented methodology proved that the presented work is significantly better in terms of various effective performance measures like execution time (0.125 s), SNR (16.4 dB), RMSE (0.074 dB), BSD (3.21%), accuracy (97.6%). [ABSTRACT FROM AUTHOR]

Due to many outliers in the pitch period detection of noisy speech signal, an improved pitch period detection method is proposed. Firstly, combining spectral subtraction and wavelet transform for noise reduction, we first windowed and framed the useful speech signal sequence obtained after noise reduction for signal endpoint and vowel subject detection. Then, in the second windowing and framing process, the multi window spectrum estimation method was introduced to further reduce the influence of some formants and residual noise on pitch detection. Finally, the improved main body extension method was used for pitch detection. The experimental simulation results show that compared with the traditional pitch detection method, the sample rate of the segment collected by the improved method is 87%, which is at least 5% higher than that of the traditional one. The improved method has higher accuracy of pitch detection. [ABSTRACT FROM AUTHOR]

Diagnostic ambiguity between chronic pulmonary diseases like asthma and Chronic Obstructive Pulmonary Disease (COPD) is very high, as they exhibit similar symptoms, which is the factor responsible for misdiagnosis, leading to heavy deaths every year. To prevent misdiagnosis, some useful work is highly required. This article presents the implementation of a computerized lung sound (LS) based method to classify asthma and COPD cases. The study is conducted on 80 asthma, 80 COPD and 80 healthy LSs. The LS denoising is carried out using empirical mode decomposition (EMD), Hurst analysis and spectral subtraction method. Wavelet entropy (WE) and wavelet packet energy (WPE) features of LS's are extracted. Various classifiers like support vector machine (SVM), decision tree (DT), k-nearest neighbor (KNN) and discriminant analysis (DA) are accessed to classify healthy, COPD and asthma using WE and WPE features of LS to produce better outcomes. Using the proposed algorithm, the study discriminates between healthy, asthma and COPD cases based on LS with a considerable classification accuracy of 99.3% using the decision tree (DT) classifier. Thus, the study confirms the successful differentiation of asthma and COPD based on LS. Future endeavours will be based on the validation of this algorithm to distinguish the real-time LS data acquired from asthmatic and COPD patients. [ABSTRACT FROM AUTHOR]

This study presents a method of adding to clean speech signals a controlled degree of “musical” noise distortions that mimic typical artefacts of speech enhancement systems. The resulting distorted speech signals were evaluated with respect to listening effort and sound quality in subjective listening tests and via model predictions. Both subjective ratings and model prediction outcomes covered the entire rating scale from “excellent”/ “no effort” to “bad”/ “extreme effort”, respectively, in a consistent way. The proposed method proved to be useful for systematic assessments of “musical” noise distortions for the conditions tested in this study.

In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spectral subtraction is an effective denoising method which is initially adopted to process MI-based EEG signals for binary BCIs in this work. On this basis, we proposed a novel feature extraction method called power spectral subtraction-based common spatial pattern (PSS-CSP) , which calculates the differences in power spectrum between binary classes of EEG signals and uses the differences in the feature extraction process. Additionally, support vector machine (SVM) algorithm is used for signal classification. Results show the proposed method (PSS-CSP) outperforms certain existing methods, achieving a classification accuracy of 76.8% on the BCIIV dataset 2b, and 76.25% and 77.38% on the OpenBMI dataset session 1 and session 2, respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper presents an improvement of the watermark extraction in speech signal watermarking. The noise added to the speech signal during transmission affects the efficiency of the watermark extraction. Removing this noise may aid to enhance the extraction process. There are methods of the speech signal enhancement, which aims to reduce the noise distortion in the speech signal. In this paper, the watermark process is done using the hybrid strategy of the Empirical Mode Decomposition (EMD) and the block-based Singular Value Decomposition (block-based SVD) with chaotic encrypted watermark. The watermark is embedded into the Singular Values matrix (SVs) because of its stability against any disturbance in the speech signal due to the different attacks. The encrypted watermark increases the security level of the watermark. When the watermark extracted at the receiver side, the speech signal will be enhanced first using spectral subtraction, Wiener filter or adaptive Wiener filter enhancement methods. The paper introduces a comparison study to evaluate the performance of each of them. Simulation results indicate that using the enhancement step improve the watermark extraction, especially using the adaptive Wiener filter. [ABSTRACT FROM AUTHOR]

Open Challenges: Continuous monitoring of fundamental cardiovascular hemodynamic parameters is essential to accomplish critical care diagnostics. Today's standard of care measures these critical parameters using multiple monitoring technologies. These state-of-the-art technologies require expensive instrumentation and complex infrastructure. Therefore, it is challenging to use current technologies to accomplish monitoring in a low resource setting. Objective: In order to address the challenges caused by having to use multiple monitoring systems, a point of care monitoring device was developed in this work to provide multiple critical parameters by uniquely measuring the hemodynamic process. Methods: To demonstrate the usability of this novel catheter multiscope, a feasibility study was performed using an animal model. The developed measurement system first acquires the dynamics of blood flow through a minimally invasive catheter. Then, a signal processing framework was developed to characterize the blood flow dynamics and to obtain critical parameters such as heart rate, respiratory rate, and blood pressure. The framework used to extract the physiological data corresponding to the acoustic field of the blood flow consisted of a noise cancellation method and wavelet-based source separation. Results: The preliminary results of the acoustic pressure field of the blood flow revealed the presence of acoustic heart and respiratory pulses. A unique framework was also developed to extract continuous blood pressure from the acoustic pressure field of the blood flow. Finally, the computed heart and respiratory rates, systolic and diastolic pressures were benchmarked with actual values measured using conventional devices to validate the hypothesis. Conclusion: The results confirm that catheter multiscope can provide multiple critical parameters with clinical reliability. Significance: A novel critical care monitoring system has been developed to accurately measure heart rate, respiratory rate, systolic and diastolic pressures from the blood flow dynamics. [ABSTRACT FROM AUTHOR]

• Colored-noise Kalman filter is adopted in our system, which is more component to deal with the complex noise and alleviate the speech distortion. • A multi-objective DNN is first employed to joint estimate parameters of the clean speech autoregressive (AR) model and the noise AR model. Two kinds of DNN, i.e., fully-connected feed-forward network (FNN) and long short-term memory (LSTM), are adopted. • A post subtraction technique is employed to further remove the residual noise in the Kalman-filtered speech. • The proposed system takes advantage of both the DNN based method and Kalman filtering, and has a good generalization capability in both seen and unseen noise environments. In this paper, we propose a new speech enhancement system using a deep neural network (DNN)-augmented colored-noise Kalman filter. In our system, both clean speech and noise are modelled as autoregressive (AR) processes, whose parameters comprise the linear prediction coefficients (LPCs) and the driving noise variances. The LPCs are obtained through training a multi-objective DNN that learns the mapping from the noisy acoustic features to the line spectrum frequencies (LSFs), while the driving noise variances are obtained by solving an optimization problem aiming to minimize the difference between the modelled and observed AR spectra of the noisy speech. The colored-noise Kalman filter with DNN estimated parameters is then applied to the noisy speech for denoising. Finally, a post-subtraction technique is adopted to further remove the residual noise in the Kalman-filtered speech. Extensive computer simulations show that the proposed speech enhancement system achieves significant performance gains when compared to conventional Kalman filter based algorithms as well as recent DNN-based methods under both seen and unseen noise conditions. [ABSTRACT FROM AUTHOR]

Enhancement of speech corrupted by broadband noise is subject of interest in many applications. For several years, the investigation of methods of denoising the vocal signal has yielded very satisfactory results, but certain problems and questions still remain. The term speech quality in speech enhancement is associated with clarity and intelligibility. So, one of these issues is to reach a compromise between noise reduction, signal distortion and musical noise. In this paper, we studied one of the classical techniques based on the spectral subtraction developed by Boll and improved by Berouti where two parameters α and β to control the effects of the distortion and the musical noise are introduced. However, the choice on these parameters (α and β) remains empirical. Our works is to find a compromise between these two parameters to obtain an optimal solution depending on the environment, the unknown noise and its level. Moreover, we propose in this paper, an algorithm based on bi-objective approach precisely Particle Swarm Optimization (PSO) technique in association with speech enhancement technique proposed by Berouti et al. Comparative results show that the performance of our proposed method with several types of noise, depending on the environment and on various noise levels, are better than those of spectral subtraction methods of Boll or Berouti. [ABSTRACT FROM AUTHOR]

In this paper, an efficient retrieval approach for encrypted speech based on biological hashing and spectral subtraction is proposed. The proposed approach improves the impact of noise on the robustness and discrimination of speech hashing scheme, as well as improves the retrieval efficiency, accuracy, and security of search digests, and realizes the authentication of the query result. The speech owner firstly secure the original speech file by encrypting it with two-dimensional Arnold mapping and uploading it to the encrypted speech library on cloud. Then, the pre-processed speech signal is subjected to spectral subtraction and noise reduction, as well as the discrete wavelet transform (DWT) is performed to obtain the wavelet low-frequency coefficient and reconstruct the speech signal, calculating the normalized autocorrelation function to obtain the matrix feature vector, and using the Chebychew mapping algorithm to generate the pseudo-random matrix, and generate the pseudo-random Fourier matrix by fast Fourier transform (FFT). Finally, iterate the matrix feature vector and pseudo-random matrix. After the thresholding, the hash sequence is constructed and uploaded to the system hash index table on cloud. When speech's user retrieval, the Hamming distance algorithm is used for the matching retrieval operation during the search and integrity authentication of the query result. The experimental results show that the proposed approach effectively reduces the noise of speech, with strong robustness and discrimination, and the retrieval efficiency, accuracy and security have been significantly improved. [ABSTRACT FROM AUTHOR]

In order to solve the problem of large error of delay estimation in low SNR environment, a new delay estimation method based on cross power spectral frequency domain weighting and spectrum subtraction is proposed. Through theoretical analysis and MATLAB simulation, among the four common weighting functions, it is proved that the cross-power spectral phase weighting method has a good sharpening effect on the peak value of the cross-correlation function, and it is verified that the improved spectral subtraction method generally has a good noise reduction effect under different SNR environments. Finally, the joint simulation results of the whole algorithm show that the combination of spectrum subtraction and crosspower spectrum phase method can effectively sharpen the peak value of cross-correlation function and improve the accuracy of time delay estimation in the low SNR environment. The results of this paper can provide useful help for sound source localization in complex environments. [ABSTRACT FROM AUTHOR]