Name
Abstract | ||
|---|---|---|
Using neural networks for vector quantization (VQ) is described. The authors show how a collection of neural units can be used efficiently for VQ encoding, with the units performing the bulk of the computation in parallel, and describe two unsupervised neural network learning algorithms for training the vector quantizer. A powerful feature of the new training algorithms is that the VQ codewords are determined in an adaptive manner, compared to the popular LBG training algorithm, which requires that all the training data be processed in a batch mode. The neural network approach allows for the possibility of training the vector quantizer online, thus adapting to the changing statistics of the input data. The authors compare the neural network VQ algorithms to the LBG algorithm for encoding a large database of speech signals and for encoding images |
Year | DOI | Venue |
|---|---|---|
1990 | 10.1109/49.62823 | IEEE Journal on Selected Areas in Communications |
Keywords | Field | DocType |
Neural networks,Vector quantization,Speech,Image coding,Encoding,Computer networks,Concurrent computing,Training data,Statistics,Image databases | Speech processing,Computer science,Image processing,Speech recognition,Vector quantization,Time delay neural network,Adaptive algorithm,Artificial neural network,Quantization (signal processing),Encoding (memory) | Journal |
Volume | Issue | ISSN |
8 | 8 | 0733-8716 |
Citations | PageRank | References |
42 | 4.47 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. K. Krishnamurthy | 1 | 61 | 7.75 |
| S. C. Ahalt | 2 | 114 | 13.12 |
| D. E. Melton | 3 | 42 | 4.47 |
| P. Chen | 4 | 43 | 4.90 |