Title :
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
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;
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
DOI :
10.1109/NNSP.1999.788151
