A neural-network-based system for testing speakers

Author

Er, M.J. ; Ooi, T.H. ; Toh, C.T. ; Toh, F.S.

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore

fYear

1994

fDate

22-26 Aug 1994

Firstpage

907

Abstract

The paper presents a high performance neural network based system for testing speakers. A multilayer neural network system with a backpropagation learning algorithm is employed. It consists of 53 input nodes, one hidden layer with 10 nodes and 1 output node. The normalized total harmonics distortion (THD) values of the speakers at different frequencies are fed to the input of the system. The average training time is 40 minutes (on a 486DX 50 MHz PC) for a training size of 100 patterns. The neural network based system is able to achieve a remarkable accuracy of 95%

Keywords

Hi-Fi equipment; automatic testing; backpropagation; loudspeakers; multilayer perceptrons; telecommunication computing; THD values; backpropagation learning algorithm; hidden layer; high performance neural network based system; multilayer neural network system; neural-network-based system; normalized total harmonics distortion; speaker testing; Audio recording; Erbium; Fatigue; Fault tolerance; Frequency response; Inspection; Neural networks; Stress; System testing; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '94. IEEE Region 10's Ninth Annual International Conference. Theme: Frontiers of Computer Technology. Proceedings of 1994

Print_ISBN

0-7803-1862-5

Type

conf

DOI

10.1109/TENCON.1994.369179

Filename

369179