Vibration properties of Paulownia wood for Ruan sound quality using machine learning methods

As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.

Characteristics analyzing and parametric modeling of the arc sound in CO_2 GMAW for on-line quality monitoring

For on-line monitoring of welding quality, the characteristics of the arc sound signals in short circuit CO2 GMAW were analyzed in the time and frequency domains. The arc sound presents a series of ringing-like oscillations that occur at the end of short circuit i. e. the moment of arc re-ignition, and distributes mainly in the frequency band below 10 kHz. A concept of the arc tone channel and its equivalent electrical model were suggested, which is considered a time-dependent distributed parametric system of which the transmission properties depend upon the geometric and physical characteristics of the arc and surroundings, and is excited by the sound source results from the change of arc energy so that results in arc sound. The linear prediction coding （ LPC ） model is an estimation of the tone channel. The radial basis function （ RBF ） neural networks were built for on-line pattern recognition of the gas-lack in welding, in which the input vectors were formed with the LPC coefficients. The test results proved that the LPC model of arc sound and the RBF networks are feasible in on-line quality monitoring.

Use of Total Precipitable Water Classification of A Priori Error and Quality Control in Atmospheric Temperature and Water Vapor Sounding Retrieval

This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared （IR） sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder （AIRS） radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water （TPW） are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control （QC） tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications.

A New Method for Deriving High-Vertical-Resolution Wind Vector Data from the L-Band Radar Sounding System in China

High-vertical-resolution radiosonde wind data are highly valuable for describing the dynamics of the meso-and microscale atmosphere. However, the current algorithm used in China's L-band radar sounding system for calculating highvertical-resolution wind vectors excessively smooths the data, resulting in significant underestimation of the calculated kinetic energy of gravity waves compared to similar products from other countries, which greatly limits the effective utilization of the data. To address this issue, this study proposes a novel method to calculate high-vertical-resolution wind vectors that utilizes the elevation angle, azimuth angle, and slant range from L-band radar. In order to obtain wind data with a stable quality, a two-step automatic quality control procedure, including the RMSE-F(root-mean-square error F) test and elemental consistency test are first applied to the slant range data, to eliminate continuous erroneous data caused by unstable signals or radar malfunctions. Then, a wind calculation scheme based on a sliding second-order polynomial fitting is utilized to derive the high-vertical-resolution radiosonde wind vectors. The evaluation results demonstrate that the wind data obtained through the proposed method show a high level of consistency with the high-resolution wind data observed using the Vaisala Global Positioning System and the data observed by the new Beidou Navigation Sounding System. The calculation of the kinetic energy of gravity waves in the recalculated wind data also reaches a level comparable to the Vaisala observations.

A Sound Quality Objective Evaluation Method Based on Auditory Peripheral Simulation Model

Based on auditory peripheral simulation model, a new Sound Quality Objective Evaluation (SQOE) method is presented,which can be used to model and analyze the impacts of head, shoulder and other parts of human body on sound wave trans-mission.This method employs the artificial head technique, in which the head related transfer function was taken into account tothe outer ear simulation phase.First, a bionic artificial head was designed as the outer ear model with considering the outersound field in view of theory and physical explanations.Then the auditory peripheral simulation model was built, which mimicsthe physiological functions of the human hearing, simulating the acoustic signal transfer process and conversion mechanismsfrom the free field to the peripheral auditory system.Finally, performance comparison was made between the proposed SQOEmethod and ArtemiS software, and the verifications of subjective and objective related analysis were made.Results show thatthe proposed method was economical, simple, and with good evaluation quality.

Evaluation of vibration and sound quality according to battery voltage change while moving a power seat in an automobile forward or backward

The changes in vibration, sound, and sound quality with changes in the driving voltage of a power seat motor from 12.5 to 14.5 V were measured and analyzed, which was used in real vehicles. BSR(buzz, squeak, rattle), which occurs for the power seat mechanism during sliding operation, was also evaluated. In addition, the results were expressed in terms of sound quality metrics, which measure the RPM change and sound level versus voltage to analyze their statistical correlation. Furthermore, vibration measurement and analysis were conducted simultaneously to determine the noisiest conditions and the source of the noise. The changes in RPM and voltage of a motor, in addition to vibration and noise, were measured at the same time to determine how noise, RPM, and voltage are interrelated.

Statistical Evaluation and Regression Analysis of Vehicle Sound Quality

A statistical approach to evaluate the subjective perception of the annoyance caused by the vehicle noise was presented in this paper. After recording the noises of Sanfeng, Huali and Xiali at speeds of 30, 40, 50, 60, 70 and 80 km/h respectively, the annoyance of the vehicle noises was evaluated in the testing room using paired comparison method, and the sound quality metrics and subjective annoyance were then distilled. Loudness, sharpness, roughness, periodicity and impulsiveness were selected for each of the vehicle noises. By correlation analysis method, it can be found that loudness has a higher correlation （0.91） with annoyance than other parameters. Meanwhile, sharpness, periodicity, roughness and impulsiveness have correlation with subjective perception with correlation coefficients being 0.84, -0.82, 0.62 and 0.87, respectively. The result of multiple regression analysis shows that calculated annoyance obtained by the regression equation can explain the perceptual annoyance and the regressed evaluation model is feasible to evaluate the sound quality of vehicle.

Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model

This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.

Objective Quantification of Small Motor Sound Quality Based on Psychoacoustic Metrics

With the widespread application of electrification and intelligence of automobiles,the number of electric devices with small DC motors in automobiles has gradually increased,and the in-terior of electric vehicles is quieter.The sound quality(SQ)of small motor directly affects the pas-senger experience.Therefore,the research on the SQ of small motor is of great significance.In this paper,the objective quantification of small motor sound quality was investigated based on tradi-tional psychoacoustic metrics.The time-frequency characteristics of sound signal was analyzed to quantify the subjective perception caused by the sound of small motor.And a new psychoacoustic metrics of objective evaluation which were suitable for small motor SQ evaluation were proposed,namely specific loudness energy(SLE),specific prominence ratio index(SPRI),relative pitch exceedance(RPE)and tremolo index(TI).Then,two objective evaluation models of small motor SQ were established to characterize the multi-dimensional subjective perception attributes by using multiple linear regression(MLR)and support vector regression(SVR)respectively,which can be used for the prediction and evaluation of the small motor SQ.The results show that the prediction accuracy of the model established by SVR method was higher than that of MLR,and SVR has stronger robustness.The objective evaluation model of small motors SQ established in this study is of great importance for improving the sound quality of small motors.

Sound Quality of Turn Indicator Sounds-Use of aMultidimensional Approach in the Automotive ProductDevelopment

Vehicle sounds are important factors of customer satisfaction and have a decisive influence on the product automobile andits quality impression. It becomes more and more important to connect customer requirements and technical specifications to developa vehicle sound with high quality. The turn indicator sound can be described as one sound, which gives the customer an importantfeedback of correct function performance and can be seen as one of the sounds, which play a role in the customer＇s perception ofvehicle quality. In a laboratory experimental study, the question was investigated, how a turn indicator sound must be designed to beperceived as pleasant and high-quality. A multidimensional approach was chosen to combine subjective customer assessments,objective psychophysiological responses of the study participants and physical parameters of the sounds. In total, 15 different tumindicator sounds were assessed by 48 subjects. The study shows how the connection of subjective and objective parameters cansupport product development. The multi-dimensional approach helps to derive recommendations for action to improve the soundquality of the product automobile. Also, the study shows a possibility to involve the human factor in a highly technical environment.

Quieting the neonatal intensive care unit:A quality improvement initiative

BACKGROUND The neonatal intensive care unit(NICU)is vital for preterm infants but is often plagued by harmful noise levels.Excessive noise,ranging from medical equipment to conversations,poses significant health risks,including hearing impairment and neurodevelopmental issues.The American Academy of Pediatrics recommends strict sound limits to safeguard neonatal well-being.Strategies such as education,environmental modifications,and quiet hours have shown to reduce noise levels.However,up to 60%of the noises remain avoidable.High noise exposure exacerbates physiological disturbances,impacting vital functions and long-term neurological outcomes.Effective noise reduction in the NICU is crucial for promoting optimal neonatal development.AIM To measure the sound levels in a NICU and reduce ambient sound levels by at least 10%from baseline.METHODS A quasi-experimental quality improvement project was conducted over 4 mo in a 20-bed level 3 NICU in a tertiary care medical college.Baseline noise levels were recorded continuously using a sound level meter.The interventions included targeted education,environmental modifications,and organizational changes,and were implemented through three rapid Plan-Do-Study-Act(PDSA)cycles.Weekly feedback and monitoring were conducted,and statistical process control charts were used for analysis.The mean noise values were compared using the paired t-test.RESULTS The baseline mean ambient noise level in the NICU was 67.8 dB,which decreased to 50.5 dB after the first cycle,and further decreased to 47.4 dB and 51.2 dB after subsequent cycles.The reduction in noise levels was 21%during the day and 28%PDSA cycle(mean difference of−17.3 dB,P<0.01).Peak noise levels decreased from 110 dB to 88.24 dB after the intervention.CONCLUSION A multifaceted intervention strategy reduced noise in the NICU by 25%over 4 months.The success of this initiative emphasizes the significance of comprehensive interventions for noise reduction.

Environmental Anthropological Study of Watershed Management‐Water Quality Conservation of Forest as a Catchment Area in the Southern Part of Australia

Authors have conducted an experiment of irradiation using sound waves (frequency) including ultrasonic waves into water such as drinking water, sea water and forest water and wastewater so far. As a result, almost the same effect of improvement of water quality was confirmed for each sound wave. Then, an environmental anthropological study of watershed management based on the sound was carried out assuming that a water quality management using the sound could be possible. The Goulburn River basin in the southern part of Australia in which indigenous peoples (Yorta Yorta) have been concerned with the management for a long time so far was selected as an objective drainage basin this time. As a result, a couple of environmental anthropological perspectives on watershed management were proposed.

声刺激联合认知行为治疗慢性耳鸣的初步探讨

目的 探讨声刺激联合认知行为治疗(CBT)对慢性耳鸣的临床疗效。方法 本研究将49例慢性耳鸣患者按照随机数字表法分为干预组(25例)和对照组(24例),所有患者均行声音刺激1个月。干预组患者同时接受持续1个月的个体化的CBT,对照组患者不接受个体化CBT。所有患者在治疗前和治疗后30 d均需填写问卷,包括耳鸣残疾评估量表(THI)、焦虑自评量表(SAS)及匹兹堡睡眠质量指数(PSQI)。结果 两组患者均完成临床试验。治疗1个月后观察,对照组患者THI、SAS、PSQI的平均值分别下降15.58、15.46、4.96,干预组患者THI、SAS、PSQI的平均值分别下降40.52、41.76、7.72,差异具有统计学意义(P<0.05);且干预组患者THI、SAS、PSQI的得分均明显低于对照组。结论 声音刺激和CBT可以预测治疗效果,声音刺激联合CBT的治疗效果优于单独的声音刺激。性别、年龄和听力障碍是耳鸣最相关的危险因素。

A PROPOSED LEED STANDARD FOR INDOOR ACOUSTICAL QUALITY

Acoustical quality of the indoor environment is increasingly being recognized as important in commercial, residentialand institutional building design. Unwanted sound is the most prevalent annoyance in many modern structures,leading to increased stress, loss of productivity and decreased quality of life for building occupants. The authors proposea minimum LEED standard for acoustical quality which can be incorporated into initial design or employed asa post-construction evaluation tool.

采用生理响度感知机制的车内噪声声品质预测

现有车内噪声声品质预测的响度计算中没有考虑真实人耳生理解剖结构的传声特性,因此提出了一种基于生理响度感知模型的车内噪声声品质预估方法。首先,采集两款轿车的车内噪声样本,并通过主观评价试验得到车内噪声的主观评价烦恼度。之后,整合中耳集总参数模型与耳蜗传输线模型,构建生理响度模型。然后,以生理响度模型的响度计算值为主要参数,结合尖锐度、粗糙度与车内噪声的主观评价值,通过TabNet模型构建了车辆声品质预测模型。最终,对比分析了所构建声品质模型与基于现有标准响度模型所构建的声品质模型的预测效果。结果表明,采用生理响度模型的声品质预测平均误差百分比仅有4.73%,优于采用Moore响度(6.13%)与Zwicker响度(6.94%)的声品质预测结果。此外,所构建的TabNet声品质预测模型的平均误差百分比也低于基于BP神经网络模型的平均误差百分比(7.60%)。采用生理响度模型的TabNet声品质预测能够提高车内噪声声品质客观评价的准确率。

基于深度学习的发动机声品质预测模型研究

为建立发动机辐射噪声品质深度学习预测模型,搭建试验台架采集发动机辐射噪声,计算噪声信号心理学客观参数并进行主观评价试验。采用卷积神经网络(convolution neural net‐work,CNN)提取信号特征,引入长短期记忆网络(long short-term memory network,LSTM)模型捕获信号长期依赖信息,利用注意力(Attention)机制使模型自动学习关键特征信息。以心理学客观参数为输入,主观评价得分为输出,建立CNN-LSTM-Attention声品质预测模型,引入改进麻雀搜索算法(improved sparrow search algorithm,ISSA)优化模型超参数,提高预测准确性。研究结果表明,ISSA-CNN-LSTM-Attention模型对发动机声品质具有良好的训练性能和预测能力,训练集和测试集的决定系数分别为0.988、0.981,训练集和测试集的平均绝对误差分别为0.204、0.241。该模型能够准确地反映客观评价参数与主观满意度之间的非线性映射关系,为发动机声品质预测提供了新的思路和方法。

电动汽车座舱内部噪声的非线性心理声学烦恼度建模

拟研究电动汽车座舱内部噪声特性对声学舒适性的影响。通过道路试验获取4种典型路面工况下某型电动汽车在不同车速下的舱内噪声,计算分析其心理声学参数,开展烦恼度主观评价实验,并基于非线性Zwicker心理声学烦恼度及其修正模型进行建模研究。结果显示,随着车速和路面粗糙程度变大,响度逐渐提高,尖锐度也随车速增大而变大;Zwicker心理声学烦恼度模型更适用于电动汽车座舱内部噪声。

电动汽车电驱动系统加速工况声品质评价及预测

电动汽车电驱动系统高频加速噪声严重影响整车声品质。为此,通过电驱动系统振动噪声试验,采集多工况加速噪声信号,并进行主、客观评价。结合相关性分析以心理声学参数为输入,通过改进的灰狼算法(improved gray wolf optimizer,IGWO)优化支持向量回归(support vector regression,SVR),建立IGWO-SVR模型用于电驱动系统声品质预测。引入互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)与信号的均方根值(root mean square,RMS),提取电驱动系统加速噪声的CEEMD-RMS特征,并建立以CEEMD-RMS为输入的IGWO-SVR声品质预测模型。检验结果表明:以CEEMD-RMS特征为输入的声品质预测模型,预测效果较以心理声学参数为输入的IGWO-SVR模型更优,测试集平均相对误差由8.88%减小为4.18%。

高速列车包间噪声特性及降噪方案设计验证

针对高速列车包间噪声问题及降噪措施进行了相关研究。首先,通过线路试验分析了高速列车包间噪声特性,包括噪声频谱、声源识别和声品质,掌握了包间中心与铺面上方测点的噪声差异,明确了包间噪声控制目标;其次,基于统计能量分析方法建立了高速列车包间噪声预测模型,并进行了试验验证,开展了包间声能量贡献分析,确定了降噪方案设计指标,进而对车体进行了隔声方案优化设计;最后,通过包间噪声的迭代预测,确定了车体降噪方案的可行性,并进行了线路试验验证。结果表明,采用笔者设计的降噪措施,包间噪声总值降低了3.2 dB(A),包间原本较高的响度和粗糙度得到明显控制,尖锐度也略有降低,声品质得到了优化。

基于声音信号的汽车安全带卷收器质量检测方法研究

为提高汽车安全带生产现场质量检测效率,根据《QC/T987—2014汽车安全带卷收器性能要求和试验方法》搭建实验平台,采集卷收器合格品与次品工作过程中的声音信号,将卷积注意力模块(CBAM)嵌入残差网络(ResNet-18)残差块之前,设计CBAM-ResNet-18“Before Blocks”模型,对采集到的卷收器声音信号进行分类。与不加注意力机制的ResNet-18模型、在残差块后加注意力机制的CBAM-ResNet-18“Within Blocks”模型、传统分类模型支持向量机和随机森林相比,模型在卷收器声音信号分类任务中的混淆矩阵、准确率、精确率、召回率和F 1值等方面均表现良好。实验结果表明:所设计的模型对于基于声音信号的汽车安全带卷收器质量检测十分有效。