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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wu, F.H. Ganesan, K. |
| Copyright Year | 1989 |
| Description | Author affiliation: US West Advanced Technol., Inc., Englewood, CO, USA (Wu, F.H.; Ganesan, K.) |
| Abstract | The authors present results of a comparative study of the efficiency of neural-net based approaches (Kohonen and NNVQ) and conventional (LBG and K-means) approaches for vector quantization. They focus on the accuracy and speed of the four methods for the VQ (vector quantization) design problem using two different input sources: a Gaussian Markov source and a speech signal (digit strings). It is shown that the LBG (Y. Linde, A. Buzo, and R. M. Gray 1980) and NNVQ methods achieve more accurate vector quantization than the K-means and Kohonen methods. The NNVQ method offers computational advantages, since the neural-net-based algorithm can be implemented with the use of parallel processors due to its inherent parallelism.< |
| Starting Page | 751 |
| Ending Page | 754 |
| File Size | 306196 |
| Page Count | 4 |
| File Format | |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.1989.266536 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1989-05-23 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Speech Quantization Gaussian processes Clustering algorithms Concurrent computing Bit rate Source coding Neural networks Computational efficiency |
| Content Type | Text |
| Resource Type | Article |
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