Car Cabin Interior Noise Classification Using Temporal Composite Features and Probabilistic Neural Network Model

Abstract:

Determination of vehicle comfort is important because continuous exposure to the noise and vibration leads to health problems for the driver and passengers. In this paper, a vehicle comfort level classification system has been proposed to detect the comfort level in cars using artificial neural network. A database consisting of sound samples obtained from 30 local cars is used. In the stationary condition, the sound pressure level is measured at 1300 RPM, 2000 RPM and 3000 RPM. In the moving condition, the sound is recorded while the car is moving at 30 km/h up to 110 km/h. Subjective test is conducted to find the Jurys evaluation for the specific sound sample. The correlation between the subjective and the objective evaluation is also tested. The relationship between the subjective results and the sound metrics is modelled using Probabilistic Neural Network. It is found from the research that the Temporal Composite Feature gives better classification accuracy for both stationary and moving condition model, 89.51% and 85.61% respectively.

• Periodical: Applied Mechanics and Materials (Volume 471)
• Pages: 64-68
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.471.64
• Online since: December 2013
• Authors: Paulraj Paulraj, Allan Andrew Melvin, Yaacob Sazali
• Keywords: Frequency Band, Neural Network (NN), Noise Comfort, Subjective Evaluation

References

[1] M. C. G. da Silva, Measurements of comfort in vehicles, Measurement Science and Technology, Institute of Physics Publishing. 13 (2002) R41-R60.

DOI: 10.1088/0957-0233/13/6/201

[2] S-K Lee and H-C Chae, The application of artificial neural networks to the characterization of interior noise booming in passenger cars, Proceedings of Instn Mechanical Engineers (IMechE), Part D: J Automobile Engineering. 218 (2004).

DOI: 10.1243/095440704322829146

[3] M. J. M. Nor, M. H. Fouladi, H. Nahvi, and A. K. Ariffin, Index for vehicle acoustical comfort inside a passenger car, Applied Acoustics, Elsevier. 69 (2008) 343-353.

DOI: 10.1016/j.apacoust.2006.11.001

[4] S. Lee, T. Kim, & U. Lee, Sound Quality Evaluation Based on Artificial Neural Network, Lecture Notes in Computer Science (LNCS). 4221 (2006) 545-554.

DOI: 10.1007/11881070_75

[5] Y. Wang, C. M. Lee, H. He and Y. Tian, A New Intelligent Technique for Sound Quality Evaluation of Nonstationary Vehicle Noises, IEEE. (2006) 33-36.

DOI: 10.1109/ifost.2006.312239

[6] Acoustics- Measurement of noise inside motor vehicles, ISO Standards, (1995).

[7] Y.S. Wang, C. M, Lee, D.G. Kim and Y. Xu, Sound- quality prediction for nonstationary vehicle interior noise based on wavelet pre-processing neural network model, Journal of Sound and Vibration, Elsevier. 299 (2007) 933-947.

DOI: 10.1016/j.jsv.2006.07.034

[8] S N Sivanandam and M Paulraj, Introduction to Artificial Neural Networks, Vikas Publishing House Pvt. Ltd., (2006).