Search Results (14)

This paper proposes an optimized control filter (OCF) based on the Filtered-x Least Mean Square (FxLMS) algorithm for multi-channel active noise control (ANC) systems. The proposed OCF-McFxLMS algorithm delivers three key contributions. Firstly, even in difficult noise situations such as White Gaussian, Brownian, and pink noise, it greatly reduces error, reaching nearly zero mean squared error (MSE) values across all Microphone (Mic) channels. Secondly, it improves computational efficiency by drastically reducing execution time from 58.17 s in the standard McFxLMS algorithm to just 0.0436 s under White Gaussian noise, enabling real-time noise control without compromising accuracy. Finally, the OCF-McFxLMS demonstrates robust noise attenuation, achieving signal-to-noise ratio (SNR) values of 137.41 dB under White Gaussian noise and over 100 dB for Brownian and pink noise, consistently outperforming traditional approaches. These contributions collectively establish the OCF-McFxLMS algorithm as an efficient and effective solution for real-time ANC systems, delivering superior noise reduction and computational speed performance across diverse noise environments. Full article

This study builds on the early brain dynamics work of Erol Başar, focusing on the human electroencephalogram (EEG) in relation to the generation of event-related potentials (ERPs) and behaviour. Scalp EEG contains not only oscillations but non-wave noise elements that may not relate to functional brain activity. These require identification and removal before the true impacts of brain oscillations can be assessed. We examined EEG/ERP/behaviour linkages in young adults during an auditory equiprobable Go/No-Go task. Forty-seven university students participated while continuous EEG was recorded. Using the PaWNextra algorithm, valid estimates of pink noise (PN) and white noise (WN) were obtained from each participant’s prestimulus EEG spectra; within-participant subtraction revealed noise-free oscillation spectra. Frequency principal component analysis (f-PCA) was used to obtain noise-free frequency oscillation components. Go and No=Go ERPs were obtained from the poststimulus EEG, and separate temporal (t)-PCAs obtained their components. Exploratory multiple regression found that alpha and beta prestimulus oscillations predicted Go N2c, P3b, and SW1 ERP components related to the imperative Go response, while PN impacted No-Go N1b and N1c, facilitating early processing and identification of the No-Go stimulus. There were no direct effects of prestimulus EEG measures on behaviour, but the EEG-affected Go N2c and P3b ERPs impacted Go performance measures. These outcomes, derived via our mix of novel methodologies, encourage further research into natural frequency components in the noise-free oscillations immediately prestimulus, and how these affect task ERP components and behaviour. Full article

Operational Modal Analysis (OMA) refers to the modal analysis with only output vibration signals of a structure in its operating state. Classic OMA has developed multiple recognition methods in both the time and frequency domains, where when the random excitation is unknown, the excitation chromaticity is usually treated as white color, which can often cause errors and affect the accuracy of identifying frequencies or damping ratios. In this article, the chromaticity recognition function is defined and a method Chromaticity Recognition Technology (CRT) for identifying noise chromaticity based on system response is proposed. Then, a simulation example is presented. The noise chromaticity is identified for the response of the system under four types of colored noise excitation, and the results of the identification of operational mode parameters with and without CRT are compared. Furthermore, the sensitivity of traditional OMA to different colored noise has been investigated. An experiment with a cantilever under base excitation of pink noise has been undertaken and the results demonstrate the feasibility of the proposed CRT in this paper. Full article

In various applications, multiscale entropy (MSE) is often used as a feature to characterize the complexity of the signals in order to classify them. It consists of estimating the sample entropies (SEs) of the signal under study and its coarse-grained (CG) versions, where the CG process amounts to (1) filtering the signal with an average filter whose order is the scale and (2) decimating the filter output by a factor equal to the scale. In this paper, we propose to derive a new variant of the MSE. Its novelty stands in the way to get the sequences at different scales by avoiding distortions during the decimation step. To this end, a linear-phase or null-phase low-pass filter whose cutoff frequency is well suited to the scale is used. Interpretations on how the MSE behaves and illustrations with a sum of sinusoids, as well as white and pink noises, are given. Then, an application to detect attentional tunneling is presented. It shows the benefit of the new approach in terms of p value when one aims at differentiating the set of MSEs obtained in the attentional tunneling state from the set of MSEs obtained in the nominal state. It should be noted that CG versions can be replaced not only for the MSE but also for other variants. Full article

When the adaptive filter length is increased, the calculation complexity increases rapidly because the relationship between the calculation and the adaptive filter length N contains a power function with no secondary path identification algorithm. Under the basic premise of unreduced noise reduction, herein, a simplified frequency-domain feedback active noise control algorithm is proposed. To reduce the computation complexity, the total delay is adopted as the estimated secondary path; the filtered reference signal is produced in the frequency domain by using multiplication to replace convolution calculation in the time domain and then updating the adaptive filter coefficients in the frequency domain. Therefore, the computational complexity has a logarithmic function with the increased adaptive filter length in the proposed algorithm. If the adaptive filter length is 512, the existing WSMANC algorithm’s calculation is 271,360 real number multiplications, while that of the proposed algorithm is only 38,912 real number multiplications. To verify the proposed algorithm’s stability, convergence speed, and noise reduction, the single-frequency noise, narrowband white noise, and narrowband pink noise, respectively, are used as the primary noise types in the simulations. The results show that (1) the proposed SFDFBANC algorithm can obtain similar noise reduction performance to existing algorithm, (2) the convergence rate is faster than existing algorithm, and (3) if the adaptive filter length is more than 64, the proposed algorithm exhibits a lower computational complexity. Full article

Dalbergia pinnata (Lour.) Prain (D. pinnata) is a valuable medicinal plant, and its volatile parts have a pleasant aroma. In recent years, there have been a large number of studies investigating the effect of aroma on human performance. However, the effect of the aroma of D. pinnata on human psychophysiological activity has not been reported. Few reports have been made about the effects of aroma and sound on human electroencephalographic (EEG) activity. This study aimed to investigate the effects of D. pinnata essential oil in EEG activity response to various auditory stimuli. In the EEG study, 30 healthy volunteers (15 men and 15 women) participated. The electroencephalogram changes of participants during the essential oil (EO) of D. pinnata inhalation under white noise, pink noise and traffic noise stimulations were recorded. EEG data from 30 electrodes placed on the scalp were analyzed according to the international 10–20 system. The EO of D. pinnata had various effects on the brain when subjected to different auditory stimuli. In EEG studies, delta waves increased by 20% in noiseless and white noise environments, a change that may aid sleep and relaxation. In the presence of pink noise and traffic noise, alpha and delta wave activity (frontal pole and frontal lobe) increased markedly when inhaling the EO of D. pinnata, a change that may help reduce anxiety. When inhaling the EO of D. pinnata with different auditory stimuli, women are more likely to relax and get sleepy compared to men. Full article

Tonal powertrain noise can have a strong negative impact on vehicle sound quality. Therefore, an assessment of the perceptibility of tonal noise with respect to masking noise is essential for the vehicle development process. In electric vehicles, due to the missing masking by the combustion engine, new methods are required for this purpose. In this study, listening tests were conducted to determine the masking threshold in the electric vehicle interior for various driving speeds (30 km/h, 60 km/h, and 90 km/h) with an Adaptive-Forced-Choice method. The novelty of this study is that it used vehicle interior noise as a masker, compared to broadband or narrowband white and pink noises. It could be shown that the masking threshold in electric vehicles strongly depends on the driving speed, and the investigated interior noise mainly affects frequencies up to 6400 Hz in this speed range. For frequencies greater than 6400 Hz, the masking noise has no significant effect on perceptibility of tonal noise in the investigated vehicle, and only the subjects’ individual absolute threshold of hearing is relevant. Additionally, a strong variation in the masking threshold between the subjects was found for high frequencies. With these results, methods that estimate masking thresholds in electric vehicles can be improved. Furthermore, threshold targets can be adjusted for different customer groups. Full article

Entropy quantification approaches have gained considerable attention in engineering applications. However, certain limitations persist, including the strong dependence on parameter selection, limited discriminating power, and low robustness to noise. To alleviate these issues, this paper introduces two novel algorithms for time series analysis: the ensemble improved permutation entropy (EIPE) and multiscale EIPE (MEIPE). Our approaches employ a new symbolization process that considers both permutation relations and amplitude information. Additionally, the ensemble technique is utilized to reduce the dependence on parameter selection. We performed a comprehensive evaluation of the proposed methods using various synthetic and experimental signals. The results illustrate that EIPE is capable of distinguishing white, pink, and brown noise with a smaller number of samples compared to traditional entropy algorithms. Furthermore, EIPE displays the potential to discriminate between regular and non-regular dynamics. Notably, when compared to permutation entropy, weighted permutation entropy, and dispersion entropy, EIPE exhibits superior robustness against noise. In practical applications, such as RR interval data classification, bearing fault diagnosis, marine vessel identification, and electroencephalographic (EEG) signal classification, the proposed methods demonstrate better discriminating power compared to conventional entropy measures. These promising findings validate the effectiveness and potential of the algorithms proposed in this paper. Full article

This paper presents a new speech-enhancement approach based on an enhanced empirical wavelet transform, considering the time and scale adaptation of thresholds for individual component signals obtained from the used transform. The time adaptation is performed using the Teager energy operator on the individual component signals, and the scale adaptation of thresholds is performed by the modified level-dependent threshold principle for the individual component signals. The proposed approach does not require an explicit estimation of the noise level or a priori knowledge of the signal-to-noise ratio as is usually needed in most common speech-enhancement methods. The effectiveness of the proposed method has been assessed based on over 1000 speech recordings from the public Librispeech database. The research included various types of noise (among others white, violet, brown, blue, and pink) and various types of disturbance (among others traffic sounds, hair dryer, and fan), which were added to the selected test signals. The score of perceptual evaluation of speech quality, allowing for the assessment of the quality of enhanced speech, and signal-to-noise ratio, allowing for the assessment of the effectiveness of disturbance attenuation, are selected for the evaluation of the resultant effectiveness of the proposed approach. The resultant effectiveness of the proposed approach is compared with other selected speech-enhancement methods or denoising techniques available in the literature. The experimental research results show that the proposed method performs better than conventional methods in many types of high-noise conditions in terms of producing less residual noise and lower speech distortion. Full article

Auditory analysis is an essential method that is used to recognize voice identity in court investigations. However, noise will interfere with auditory perception. Based on this, we selected white noise, pink noise, and speech noise in order to design and conduct voice identity perception experiments. Meanwhile, we explored the impact of the noise type and frequency distribution on voice identity perception. The experimental results show the following: (1) in high signal-to-noise ratio (SNR) environments, there is no significant difference in the impact of noise types on voice identity perception; (2) in low SNR environments, the perceived result of speech noise is significantly different from that of white noise and pink noise, and the interference is more obvious; (3) in the speech noise with a low SNR (−8 dB), the voice information contained in the high-frequency band of 2930~6250 Hz is helpful for achieving accuracy in voice identity perception. These results show that voice identity perception in a better voice transmission environment is mainly based on the acoustic information provided by the low-frequency and medium-frequency bands, which concentrate most of the energy of the voice. As the SNR gradually decreases, a human’s auditory mechanism will automatically expand the receiving frequency range to obtain more effective acoustic information from the high-frequency band. Consequently, the high-frequency information ignored in the objective algorithm may be more robust with respect to identity perception in our environment. The experimental studies not only evaluate the quality of the case voice and control the voice recording environment, but also predict the accuracy of voice identity perception under noise interference. This research provides the theoretical basis and data support for applying voice identity perception in forensic science. Full article

The increased demand for well-being has fueled interest in sleep. Research in technology for monitoring sleep ranges from sleep efficiency and sleep stage analysis to sleep disorder detection, centering on wearable devices such as fitness bands, and some techniques have been commercialized and are available to consumers. Recently, as interest in digital therapeutics has increased, the field of sleep engineering demands a technology that helps people obtain quality sleep that goes beyond the level of monitoring. In particular, interest in sleep aids for people with or without insomnia but who cannot fall asleep easily at night is increasing. In this review, we discuss experiments that have tested the sleep-inducing effects of various auditory stimuli currently used for sleep-inducing purposes. The auditory stimulations were divided into (1) colored noises such as white noise and pink noise, (2) autonomous sensory meridian response sounds such as natural sounds such as rain and firewood burning, sounds of whispers, or rubbing various objects with a brush, and (3) classical music or a preferred type of music. For now, the current clinical method of receiving drugs or cognitive behavioral therapy to induce sleep is expected to dominate. However, it is anticipated that devices or applications with proven ability to induce sleep clinically will begin to appear outside the hospital environment in everyday life. Full article

In acoustic practice, established methods of measuring the acoustic properties of partition structures are used. Recommended procedures and means can be found in technical standards, but practice suggests that measurement results may also depend on measurement conditions. These procedures leave the choice of noise type, frequency interval examined, and excitation interval on the measurer. The aim of this research is to determine which parameter has a significant effect on the results, and to quantify the extent of this effect. We examined the type of noise, the frequency band of the sound passing through the partition structure and the excitation interval of the diffusion field in the rooms (hereinafter referred to as “excitation interval”). During the research, we conducted a number of repeated, statistically significant measurements, which we first evaluated by classical methods used in acoustic practice. We subjected the obtained results to a thorough mathematical analysis. Evaluation of the results shows that some measurement conditions significantly affect the resulting values, especially in the low-frequency spectrum. One of the most important elements which has an effect on the results is the type of excitation noise, which, when assessed in the source room, excites the diffuse sound field, and its transmission through the considered partition structure is measured. The significance of the investigated frequency interval was also demonstrated. Full article

The complexity and the disorder of a 1/$f^{\alpha }$ noise time series are quantified by entropy of entropy (EoE) and average entropy (AE), respectively. The resulting EoE vs. AE plot of a series of 1/$f^{\alpha }$ noises of various values of $\alpha$ exhibits a distinct inverted U curve. For the 1/$f^{\alpha }$ noises, we have shown that $\alpha$ decreases monotonically as AE increases, which indicates that $\alpha$ is also a measure of disorder. Furthermore, a 1/$f^{\alpha }$ noise and a cardiac interbeat (RR) interval series are considered equivalent as they have the same AE. Accordingly, we have found that the 1/$f^{\alpha }$ noises for $\alpha$ around 1.5 are equivalent to the RR interval series of healthy subjects. The pink noise at $\alpha$ = 1 is equivalent to atrial fibrillation (AF) RR interval series while the white noise at $\alpha$ = 0 is more disordered than AF RR interval series. These results, based on AE, are different from the previous ones based on spectral analysis. The testing macro-average F-score is 0.93 when classifying the RR interval series of three groups using AE-based $\alpha$, while it is 0.73 when using spectral-analysis-based $\alpha$. Full article

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter $\mathsf{\gamma }$—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults. Full article