Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assembly errors,and other imperfections that may arise during the design or manufacturing phases.Conse-quently,these micro-motors might generate anomalous noises during their operation,consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers.Automobile micro-motors exhibit a diverse array of structural variations,consequently leading to the manifestation of a multitude of distinctive auditory irregularities.To address the identification of diverse forms of abnormal noise,this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network(VAF-CNN).This method entails the deployment of distinct network branches,each serving to capture disparate features from the multi-sensor data,all the while considering the auditory perception traits inherent in the human auditory sys-tem.The intermediary layer integrates the concept of adaptive weighting of multi-sensor features,thus affording a calibration mechanism for the features hailing from multiple sensors,thereby enabling a further refinement of features within the branch network.For optimal model efficacy,a feature fusion mechanism is implemented in the concluding layer.To substantiate the efficacy of the proposed approach,this paper initially employs an augmented data methodology inspired by modified SpecAugment,applied to the dataset of abnormal noise sam-ples,encompassing scenarios both with and without in-vehicle interior noise.This serves to mitigate the issue of limited sample availability.Subsequent comparative evaluations are executed,contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data.The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference.Moreover,it holds notable practical significance in the engineering domain,as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.

An auditory periphery model for improving narrow-band noise recognition rate of underwater targets

The recognition rate of the auditory periphery features decreases when the model is used to identify underwater targets in practice. To solve this problem, an improved method based on Gammatone filter bank is proposed. Firstly, after the reason of the decreasing of the recognition results is analyzed, the mechanism of multichannel data acquisition in acoustic engineering may narrow down signal frequency range, which leads to time-frequency features distortion. Secondly, the Gammatone filter bank is implemented to simulate frequency decom- position characteristics of human ear basilar membrane. Since the class information of the underwater noise signal is mostly contained in low frequency range, the auditory features of the conventional model are interpolated and the channel number of the filter bank and the central frequency of each frequency band are adjusted accordingly to obtain a 27-dimensional feature vector of the narrow-band target signal. The adjusted model may reflect the target＇s time- frequency feature more precisely. Finally, the performance of the auditory features is tested by a Neural Network classifier. The experiment results show that the modified auditory model is more effective than the conventional ones. The major information contained in broadband signals is reserved and the classification ability for real targets is further enhanced. The recog- nition results are increased from 82.59% to 88.80%. The modified auditory features effectively improve the recognition rate for underwater target radiated noise signals.

Mobile Communication Voice Enhancement Under Convolutional Neural Networks and the Internet of Things

This study aims to reduce the interference of ambient noise in mobile communication,improve the accuracy and authenticity of information transmitted by sound,and guarantee the accuracy of voice information deliv-ered by mobile communication.First,the principles and techniques of speech enhancement are analyzed,and a fast lateral recursive least square method(FLRLS method)is adopted to process sound data.Then,the convolutional neural networks(CNNs)-based noise recognition CNN(NR-CNN)algorithm and speech enhancement model are proposed.Finally,related experiments are designed to verify the performance of the proposed algorithm and model.The experimental results show that the noise classification accuracy of the NR-CNN noise recognition algorithm is higher than 99.82%,and the recall rate and F1 value are also higher than 99.92.The proposed sound enhance-ment model can effectively enhance the original sound in the case of noise interference.After the CNN is incorporated,the average value of all noisy sound perception quality evaluation system values is improved by over 21%compared with that of the traditional noise reduction method.The proposed algorithm can adapt to a variety of voice environments and can simultaneously enhance and reduce noise processing on a variety of different types of voice signals,and the processing effect is better than that of traditional sound enhancement models.In addition,the sound distortion index of the proposed speech enhancement model is inferior to that of the control group,indicating that the addition of the CNN neural network is less likely to cause sound signal distortion in various sound environments and shows superior robustness.In summary,the proposed CNN-based speech enhancement model shows significant sound enhancement effects,stable performance,and strong adapt-ability.This study provides a reference and basis for research applying neural networks in speech enhancement.

Target recognition based on phase noise of received laser signal in lidar jammer

In this Letter, a method based on the effects of imperfect oscillators in lasers is proposed to distinguish targets in continuous wave tracking lidar. This technique is based on the fact that each lidar signal source has a specific influence on the phase noise that makes real targets from the false ones. A simulated signal is produced by complex circuits, modulators, memory, and signal oscillators. For example, a deception laser beam has an unequal and variable phase noise from a real target. Thus, the phase noise of transmitted and received signals does not have the same power levels and patterns. To consider the performance of the suggested method, the probability of detection（PD） is shown for various signal-to-noise ratios and signal-to-jammer ratios based on experimental outcomes.