Reducing transmission error effects using a self-organizing network

The author shows that a Kohonen self-organizing network can be used to reduce the effects of errors in digital transmission of speech, Kohonen learning is used to cause the network nodes to mimic the input data distribution, while self-organization is used to arrange the nodes so as to reduce sensitivity to transmission errors. The number of bit substitutions that can be tolerated without unacceptable degradation of the speech signal is roughly doubled by this method. In contrast to other applications of self-organizing networks, in this case the dimensionality of the network is very much higher than that of the input data.<<ETX>>

[1]  Nasser M. Nasrabadi,et al.  Vector quantization of images based upon the Kohonen self-organizing feature maps , 1988, ICNN.

[2]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[3]  Nasser M. Nasrabadi,et al.  Vector quantization of images based upon the Kohonen self-organizing feature maps , 1988, IEEE 1988 International Conference on Neural Networks.

[4]  Duane DeSieno,et al.  Adding a conscience to competitive learning , 1988, IEEE 1988 International Conference on Neural Networks.

[5]  John J. Komo,et al.  Random Signal Analysis in Engineering Systems , 1987 .

[6]  Bernard M. Smith Instantaneous companding of quantized signals , 1957 .

[7]  Teuvo Kohonen,et al.  The 'neural' phonetic typewriter , 1988, Computer.

[8]  J. Makhoul,et al.  Vector quantization in speech coding , 1985, Proceedings of the IEEE.