Noise-robust feature extraction using multi-layer principal...

Details Noise-robust feature extraction using multi-layer principal... Noise-robust feature extraction using multi-layer principal...

2006-06-07

2008-11-25

Dorvil, Richemond (Department: 2626)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S205000, C704S228000, C704S235000

Reexamination Certificate

active

07457749

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.

REFERENCES:
patent: 6947892 (2005-09-01), Bauer et al.
Balachander et al. Oriented Soft Localizeed Subspace Classification, IEEE ICASSP 1999.

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