Sound Quality Evaluation Based on Artificial Neural Network

  • Sang-Kwon Lee
  • Tae-Gue Kim
  • Usik Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


Booming index has been developed recently to evaluate the sound characteristics of passenger cars. Previous work maintained that booming sound quality is related to loudness and sharpness–the sound metrics used in psychoacoustics–and that the booming index is developed by using the loudness and sharpness for a signal within whole frequency between 20Hz and 20kHz. In the present paper, the booming sound quality was found to be effectively related to the loudness at frequencies below 200Hz; thus the booming index is updated by using the loudness of the signal filtered by the low pass filter at frequency under 200Hz. The relationship between the booming index and sound metric is identified by an artificial neural network (ANN).


Artificial Neural Network Subjective Rate Sound Quality Synthetic Signal Sound Level Meter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fahy, F., Walker, J.: Fundamentals of noise and vibration. S & FN SPON (1998)Google Scholar
  2. 2.
    Matsuyama, S., Maruyama, S.: Booming Noise Analysis Method Based on Acoustic Excitation Test. SAE 1998 World Congress and Exhibition, Detroit, Michigan, USA, SAE980588 (1998)Google Scholar
  3. 3.
    Hatano, S., Hashimoto, T.: Booming Index as a Measure for Evaluating Booming Sensation. In: Proceedings of Inter-Noise 2000, Nice, France (2000)Google Scholar
  4. 4.
    Murata, H., Tanaka, T.H., Ohsasa, Y.: Sound Quality Evaluation of Passenger Vehicle Interior Noise. In: Proceedings of the 1993 SAE Noise and Vibration Conference, Traverse City, Michigan, USA, SAE931347 (1993)Google Scholar
  5. 5.
    Lee, S.K.: Vibrational Power Flow and Its Application to a Passenger Car for Identification of Vibration Transmission Path. In: Proceedings of the 2001 SAE Noise and Vibration Conference, Traverse City, Michigan, USA, SAE2001-01-1451 (2001)Google Scholar
  6. 6.
    Zwicker, E., Fastl, H.: Psychoacoustics: Facts and Models, 2nd edn. Springer, Berlin (1999)Google Scholar
  7. 7.
    Lee, S.K., Chae, H.C., Park, D.C., Jung, S.G.: Sound Quality Index Development for the Booming Noise Of Automotive Sound Using Artificial Neural Network Information Theory. In: Sound Quality Symposium 2002 Dearborn, Michigan USA, CD N0.5 (2002)Google Scholar
  8. 8.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  9. 9.
    Matrn, H.: Neural Network Design. PWS Publishing Company (1996)Google Scholar
  10. 10.
    Waszczyszyn, Z., Ziemianski, L.: Neural Networks in Mechanics of Structures and Materials-New Results and Prospects of Applications Computers & Structures, pp. 2261–2276 (2001)Google Scholar
  11. 11.
    Lee, S.K.: Adaptive Signal Processing and Higher Order Time Frequency Analysis for Acoustic and Vibration Signatures in Condition Monitoring. Ph.D. Thesis, ISVR, University of Southampton (1998)Google Scholar
  12. 12.
    Herlufsen, G.H., Hansen, H.K., Vold, H.: Characteristics of the Vold-Kalman Order Tracking Filter. Bruel & Kjaer Technical Review, 1–50 (1998)Google Scholar
  13. 13.
    Lee, S.K., White, P.R.: The Enhancement of Impulsive Noise and Vibration Signals for Fault Detection in Rotating and Reciprocating Machinery. Journal of Sound and Vibration, 485–505 (1998)Google Scholar
  14. 14.
    Stevens, S.S.: Perceived Level of Noise by MarkII and Decibels. The Journal of the Acoustic Society of America, 575–601 (1971)Google Scholar
  15. 15.
    Moore, B.C.J., Glasberg, B.R.: A Revision of Zwicker’s Loudness Model. Acoustica, 335–345 (1996)Google Scholar
  16. 16.
    Bismarck, V.: Sharpness as an Attribute of the Timbre of Steady Sounds. Acoustica, 159–172 (1974)Google Scholar
  17. 17.
    Aures, W.: The Sensory Euphony as a Function of Auditory Sensations. Acoustica, 282–290 (1985)Google Scholar
  18. 18.
    Aures, W.: A Procedure for Calculating Auditory Roughness. Acoustica, 268–281 (1985)Google Scholar
  19. 19.
    Laux, P.C.: Using Artificial Neural Networks to Model the Human Annoyance to Sound. Ph.D. Thesis School of Mechanical Engineering, Purdue University (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sang-Kwon Lee
    • 1
  • Tae-Gue Kim
    • 1
  • Usik Lee
    • 1
  1. 1.Acoustic Noise Signal Processing Labbatory, Dapartment of Mechanical EngineeringInha UniversityInchonKorea

Personalised recommendations