Separation of acoustic signals using self-organizing neural networks

Author

Gautama, Temujin ; Van Hulle, Marc M.

Author_Institution

Lab. voor Neuro- en Psychofysiologie, Katholieke Univ., Leuven, Belgium

fYear

1999

fDate

36373

Firstpage

324

Lastpage

332

Abstract

Spectral modeling is an essential component in many signal processing applications, such as speech enhancement and sound monitoring. This paper demonstrates its use in the separation of acoustic sources from a compound signal that is registered by one sensor. Our technique distinguishes itself from the popular blind source separation procedure by its much higher noise insensitivity and its ability to cope with varying as well as non-square mixing conditions

Keywords

acoustic signal processing; audio signal processing; quantisation (signal); self-organising feature maps; spectral analysis; speech enhancement; acoustic signals separation; acoustic sources; compound signal; noise insensitivity; nonsquare mixing conditions; quantization models; self-organizing neural networks; sensor; signal processing applications; sound monitoring; spectral modeling; speech enhancement; Acoustic noise; Acoustic sensors; Blind source separation; Monitoring; Neural networks; Quantization; Signal processing; Signal processing algorithms; Source separation; Speech enhancement;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.

Conference_Location

Madison, WI

Print_ISBN

0-7803-5673-X

Type

conf

DOI

10.1109/NNSP.1999.788151

Filename

788151