Nonlinear modeling and prediction of forklift acoustic annoyance based on the improved neural networks

SIMULATION ◽  
2021 ◽  
pp. 003754972110648
Author(s):  
Enlai Zhang ◽  
Jiading Lian ◽  
Jingjing Zhang ◽  
Jiahe Lin
Keyword(s):  
Neural Network ◽  
Sound Pressure Level ◽  
Sound Pressure ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Nonlinear Modeling ◽  
Subjective Evaluation ◽  
Pressure Level ◽  
Subjective Perception ◽  
Artificial Neural Network Ann

Aiming at the characteristics of high decibels and multiple samples for forklift noise, a subjective evaluation method of rank score comparison (RSC) based on annoyance is presented. After pre-evaluation, comprehensive evaluation and data tests on collected 50 noise samples, the annoyance grades of all noise samples were obtained, and seven psycho-acoustic parameters including linear sound pressure level (LSPL), A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, impulsiveness and articulation index (AI) were determined by correlation calculation. Considering the nonlinear characteristics of human ear subjective perception, objective parameters, and annoyance were used as input and output variables correspondingly and then three nonlinear mathematical models of forklift acoustic annoyance were established using traditional artificial neural network (ANN), genetic-algorithm neural network (GANN), and particle-swarm-optimization neural network (PSONN). Moreover, the prediction accuracy of the three models was tested and compared by sample data. The results indicate that the average relative error (ARE) between the experimental and predicted values of acoustic annoyance based on PSONN model is 3.893%, which provides an effective technical support for further optimization and subjective evaluation.

The Study of Sound Quality for Car Compressor

2012 ◽  
Vol 226-228 ◽  
pp. 444-447 ◽  
Author(s):  
Yan Fang Hou ◽  
Guo Hua Han ◽  
Xue Ying Xu
Keyword(s):  
Sound Pressure Level ◽  
Sound Pressure ◽  
Evaluation Method ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
Sound Quality ◽  
Pressure Level ◽  
Subjective Perception ◽  
Problem Frequency ◽  
Psychoacoustic Metrics

As cars become more and more quiet the sound quality of rotary components such as car compressor becomes more important in the customer’s subjective perception of passenger car quality. This needs a new evaluation method which is not only the traditional method like sound pressure level but also Psychoacoustic Metrics to focus the specification of component sounds. This paper on one hand analyzed one car’s abnormal noise reason through the tests, found the main problem frequency band of the compressor, and on the other hand studied the compressor’s psychoacoustic metrics. In this paper the countermeasure of solving this problem was also given, and then noise level and psychoacoustic parameters are compared. Both objective evaluation and subjective evaluation showed that the compressor with the solution not only reduced the sound pressure level, but also improved the car sound quality greatly.

The Study on Influence of High Frequency Noise on Sound Quality for Generator

2011 ◽  
Vol 105-107 ◽  
pp. 74-79
Author(s):  
Zha Gen Ma ◽  
Xue Ying Xu ◽  
Guo Hua Han
Keyword(s):  
Sound Pressure Level ◽  
High Frequency ◽  
Sound Pressure ◽  
Evaluation Method ◽  
Subjective Evaluation ◽  
Internal Noise ◽  
Sound Quality ◽  
Pressure Level ◽  
Frequency Noise ◽  
High Frequency Noise

As cars become quieter the sound quality of components becomes more critical in the customer perception of car quality. This requires a need of new evaluation method for the specification of component sounds. Considering that high frequency noise plays an important roll for internal noise, the noise signals in the range from 7000Hz to 8000Hz are specially emphasized. Then the acoustic evaluation parameters, such as Sound Pressure Level, Sharpness and Steadiness have been evaluated. Judged from experiences and measuring results, an abnormal noise comes from Generator, through the exchange of Generator, Sound Pressure Level and sharpness were greatly improved. At the same time, subjective evaluation also indicated that there was no complaint any more in passenger compartment. Low Sound Pressure Level, sharpness can lead to perceived high product quality.

Just noticeable difference of sound pressure level of speech in open-plan office

2021 ◽  
Vol 263 (1) ◽  
pp. 5166-5169
Author(s):  
Haram Lee ◽  
Hyunin Jo ◽  
Jin Yong Jeon
Keyword(s):  
Sound Pressure Level ◽  
Sound Pressure ◽  
Paired Comparison ◽  
Subjective Evaluation ◽  
Simulation Models ◽  
Pressure Level ◽  
Just Noticeable Difference ◽  
Sound Sources ◽  
Sound Environment ◽  
Open Plan

In this study, the general sound environment characteristics of open-plan office (OPO) were investigated, and just noticeable difference (JND) of sound pressure level of speech at a distance of 4 m (Lp,A,S,4m) suggested in ISO 3382-3 was suggested. First, in order to understand the sound environment characteristics of OPO, one minute sound sources recorded in 8 offices were collected and physical and psychological acoustic characteristics were analyzed. A total of 30 office workers were subject to subjective evaluation on 8 sound sources, and they were asked to respond to questionnaires related to annoyance, work satisfaction, and speech privacy. Next, to investigate the JND, two computer simulation models identical to those of the actual OPO were implemented, and sound sources each having six different Lp,A,S,4m values were generated through the change of the sound absorption coefficient of the interior finish. The JND of Lp,A,S,4m was presented by performing paired comparison for the same subjects. It is expected that the JND of Lp,A,S,4m proposed in this study can be used for the sound environment rating of OPO.

scholarly journals Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 903 ◽  
Author(s):  
Juan M. Navarro ◽  
Raquel Martínez-España ◽  
Andrés Bueno-Crespo ◽  
Ramón Martínez ◽  
José M. Cecilia
Keyword(s):  
Neural Network ◽  
Sensor Network ◽  
Sound Pressure Level ◽  
Deep Neural Network ◽  
Sound Pressure ◽  
Noise Pollution ◽  
Pressure Level ◽  
Acoustic Sensor ◽  
Short Term ◽  
Sound Levels

Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory.

scholarly journals Evaluation and Improvement of the Sound Quality of a Diesel Engine Based on Tests and Simulations

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 777 ◽  
Author(s):  
Zhengwei Yang ◽  
Huihua Feng ◽  
Bingjie Ma ◽  
Ammar Abdualrahim Alnor Khalifa
Keyword(s):  
Diesel Engine ◽  
Sound Pressure Level ◽  
Sound Pressure ◽  
Subjective Evaluation ◽  
Sound Field ◽  
Sound Quality ◽  
Pressure Level ◽  
Sound Power ◽  
Improvement Strategy ◽  
Radiated Sound

Traditional acoustic evaluation of a diesel engine generally uses the A-weighted sound pressure level (AWSPL) and radiated sound power to assess the noise of an engine prototype present in an experiment. However, this cannot accurately and comprehensively reflect the auditory senses of human subjects during the simulation stage. To overcome such shortage, the Moore–Glasberg loudness and sharpness approach is applied to evaluate and improve the sound quality (SQ) of a 16 V-type marine diesel engine, and synthesizing noise audio files. Through finite element (FE) simulations, the modes of the engine’s block and the average vibrational velocity of the entire engine surface were calculated and compared with the test results. By further applying an automatically matched layer (AML) approach, the engine-radiated sound pressure level (SPL) and sound power contributions of all engine parts were obtained. By analyzing the Moore–Glasberg loudness and sharpness characteristics of three critical sound field points, an improvement strategy of the oil sump was then proposed. After improvement, both the loudness and sharpness decreased significantly. To verify the objective SQ evaluation results, ten noise audio clips of the diesel engine were then synthesized and tested. The subjective evaluation results were in accordance with the simulated analysis. Therefore, the proposed approach to analyze and improve the SQ of a diesel engine is reliable and effective.

Application of Dynamic Vibration Absorber in Noise Control of Whole Vehicle

2012 ◽  
Vol 226-228 ◽  
pp. 423-426
Author(s):  
Xue Ying Xu ◽  
Guo Hua Han ◽  
Zha Gen Ma
Keyword(s):  
Sound Pressure Level ◽  
Sound Pressure ◽  
Subjective Evaluation ◽  
Internal Noise ◽  
Pressure Level ◽  
Vibration Absorber ◽  
Frequency Noise ◽  
Dynamic Vibration Absorber ◽  
Engine Mounts ◽  
Dynamic Vibration

As cars become quieter the sound quality of components becomes more critical in the customer perception of car quality. Considering that middle frequency noise plays an important roll for internal noise, the noise signals in the range from200Hz to 500Hz are specially emphasized. Then the acoustic evaluation parameters, such as Sound Pressure Level, Acceleration have been evaluated. Judged from experiences and measuring results, an abnormal noise comes from engine mounts, through the use of dynamic vibration absorber on engine mounts, Vibration on engine mounts and Sound Pressure Level in interior vehicle were greatly improved. At the same time, subjective evaluation also indicated that there was no complaint any more in passenger compartment. Dynamic vibration absorber can properly solve the abnormal noise.

scholarly journals Indoor environmental quality and global comfort: an in-field study in workspaces

2021 ◽  
Vol 2069 (1) ◽  
pp. 012169
Author(s):  
G E Puglisi ◽  
G Spigliantini ◽  
N Oggiani ◽  
L Shtrepi ◽  
M C Masoero ◽  
...  
Keyword(s):  
Air Quality ◽  
Environmental Quality ◽  
Sound Pressure Level ◽  
Environmental Variables ◽  
Sound Pressure ◽  
Objective Evaluation ◽  
Pressure Level ◽  
Subjective Perception ◽  
Indoor Environmental Quality ◽  
Evaluation Approach

Abstract The EN 16798-1 specifies the requirements to assess indoor environmental quality (IEQ) considering thermal, air quality, lighting and acoustics domains. A drawback of the standard is that it is based on an objective evaluation approach and does not account for the subjective perception. Also, the standard does not assess global IEQ nor comfort as a single index for the interaction of all the domains. This work tests the metrics proposed in the standard relating them to the occupants’ evaluations. An in-field monitoring campaign was performed in the ARPA headquarter in Aosta (Italy), acquiring quantities to be correlated with the subjective perception of IEQ gained through surveys. An insight on the possible approach to communicate IEQ and comfort feedbacks to the occupants was investigated to promote their awareness. Preliminary results show that the occupants’ perception can be predicted by adopting the approach proposed in EN 16798-1 in the case of thermal comfort, but limitations emerge about air quality, lighting and acoustics. Such result allows investigating how the environmental variables considered by the standard (e.g., the maximum sound pressure level or the maximum CO2 concentration) can be adopted as predictors of comfort, thus how new parameters and assessment methods should be introduced.

Characterization of an axle-gear whine sound in a sports utility vehicle and its objective evaluation based on synthetic sound technology and an artificial neural network

2008 ◽  
Vol 222 (3) ◽  
pp. 383-396 ◽  
Author(s):  
S-K Lee ◽  
T G Kim ◽  
J T Lim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Evaluation Method ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sound Technology ◽  
Utility Vehicle

The gear whine sound of an axle system is one of the most important sound qualities in a sports utility vehicle (SUV). Previous work has shown that, because of masking effects, it is difficult to evaluate the gear whine sound objectively by using only the A-weighted sound pressure level. In this paper, the characteristics of the axle-gear whine sound were first investigated on the basis of synthetic sound technology, and a new objective evaluation method for this sound was developed by using sound metrics, which are the psychoacoustic parameters, and the artificial neural network (ANN) used for the modelling of the correlation between objective and subjective evaluation. This model developed by using ANN was applied to the objective evaluation of the axle-gear whine sound for real SUVs and the output of the model was compared with subjective evaluation. The results indicate a good correlation of over 90 per cent between the subjective and objective evaluations.

scholarly journals Vehicle Interior Noise Prediction Based on Elman Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8029
Author(s):  
Min Li ◽  
Wei Zhou ◽  
Jiang Liu ◽  
Xilong Zhang ◽  
Fuquan Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Sound Pressure Level ◽  
Sound Pressure ◽  
Pressure Level ◽  
The Body ◽  
Side Impact ◽  
Vehicle Body ◽  
Target Point ◽  
Vehicle Interior ◽  
Elman Neural Network

Vehicle interior noise is an important factor affecting ride comfort. To reduce the noise inside the vehicle at the vehicle body design stage, a finite element model of the vehicle body must be established. While taking the first-order global modal of the body-in-white, the maximum sound pressure level of the target point in the vehicle, the body mass, and the side impact conditions into account, the thickness of the body panel as determined via sensitivity analysis is treated as the input variable, and the sample is determined by following the Hamersley experimental design. Specifically, the Elman neural network predicts the noise value in the vehicle, and a vehicle body structure optimization method that comprehensively considers NVH performance and side impact safety is established. The prediction errors of the Elman neural network algorithm were within 3%, which meets the prediction accuracy requirements. To achieve satisfactory restraint performance, the maximum sound pressure level of the target point in the vehicle is reduced by 5.92 dB, and the maximum intrusions of the two points on the B-pillar inner panel are reduced by 31.1 mm and 33.71 mm, respectively. The side impact performance is improved while the noise inside the vehicle is reduced. This study provides a reference method for multidisciplinary research aiming to optimize the design of vehicle body structures.

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