A structure is defined that accepts waveforms as inputs and generates a sequence of symbols representing the sequence of transients present in the waveform. This structure is developed by generalizing an unsupervised learning algorithm to the time- varying case. The algorithm accepts a sequence of unlabeled waveforms to find cluster centers associated with the transients. The algorithm is shown to converge based on assumptions concerning a unique optimum. This is done by the application of stochastic- approximation theorem to a gradient- following technique. The resulting algorithm is applied to a problem in speech processing.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Information Theory|
|Publication status||Published - Mar 1972|
ASJC Scopus subject areas
- Information Systems
- Electrical and Electronic Engineering