Noise-robust feature extraction using multi-layer principal component analysis
US7082394B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 25, 2002 |
| Grant date | Jul 25, 2006 |
| Priority date | — |
| Expiry date | Nov 12, 2024 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Extracting features from signals for use in classification, retrieval, or identification of data represented by those signals uses a “Distortion Discriminant Analysis” (DDA) of a set of training signals to define parameters of a signal feature extractor. The signal feature extractor takes signals having one or more dimensions with a temporal or spatial structure, applies an oriented principal component analysis (OPCA) to limited regions of the signal, aggregates the output of multiple OPCAs that are spatially or temporally adjacent, and applies OPCA to the aggregate. The steps of aggregating adjacent OPCA outputs and applying OPCA to the aggregated values are performed one or more times for extracting low-dimensional noise-robust features from signals, including audio signals, images, video data, or any other time or frequency domain signal. Such extracted features are useful for many tasks, including automatic authentication or identification of particular signals, or particular elements within such signals.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.