Noise-robust feature extraction using multi-layer principal component analysis
First Claim
1. A system for training a feature extractor for extracting features from an input signal comprising:
- receiving at least one training signal;
receiving at least one distorted copy of the at least one training signal;
transforming each training signal and each distorted copy of the at least one training signal into a suitable representation for taking projections;
performing a multi-layer oriented principal component analysis (OPCA) of the at least one transformed training signal and the at least one transformed distorted copy of the at least one training signal to compute a set of training projections for each layer; and
constructing a signal feature extractor from two or more layers of said projections.
2 Assignments
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Accused Products
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.
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Citations
42 Claims
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1. A system for training a feature extractor for extracting features from an input signal comprising:
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receiving at least one training signal; receiving at least one distorted copy of the at least one training signal; transforming each training signal and each distorted copy of the at least one training signal into a suitable representation for taking projections; performing a multi-layer oriented principal component analysis (OPCA) of the at least one transformed training signal and the at least one transformed distorted copy of the at least one training signal to compute a set of training projections for each layer; and constructing a signal feature extractor from two or more layers of said projections. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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22. A method for training a feature extractor for extracting features from an input signal comprising using a computing device to:
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divide at least one training signal into a set of adjacent frames, each frame having a same size; apply a first oriented principal component analysis (OPCA) to the adjacent frames to produce a first set of generalized eigenvectors for each frame; choose a number N of highest value eigenvectors for each frame; project each frame along the eigenvectors computed for each frame to produce a first set of N projections for each frame; aggregate the projections for adjacent frames to produce at least one aggregate; apply a second OPCA to each aggregate, with the second OPCA producing a second set of generalized eigenvectors for each aggregate frame; choose N highest value elgenvectors produced by the second OPCA for each aggregate frame; project each aggregate frame along the eigenvectors computed for the each aggregate frame to produce a second set of N projections for each aggregate frame; and train a feature extractor by assigning the first set of N projections to a first feature extractor layer, and assigning the second set of N projections to a second feature extractor layer. - View Dependent Claims (23, 24, 25, 26)
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27. A computer-readable medium having computer executable instructions for extracting features from an input signal, said computer executable instructions comprising:
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applying a multi-layer oriented principal component analysis (OPCA) to a set of at least one training signals for producing a set of training projections for each OPCA layer, wherein each subsequent layer of the OPCA is performed on an aggregate of outputs from an immediately preceding OPCA layer; training a feature extractor by assigning the set of training projections for each OPCA layer to a corresponding layer of the feature extractor; and extracting features from at least one input signal by passing each input signal through each layer of the feature extractor in the order in which the layers were originally computed. - View Dependent Claims (28, 29)
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30. A computer-implemented process for training an audio signal feature extractor, comprising using a computing device to:
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receive an audio input comprising representative audio data; transform the audio input into a time-frequency representation; compute generalized eigenvalues over the transformed audio data; compute at least one eigenvector corresponding to at least one highest value elgenvalue and assign those elgenvectors to a first layer of an audio signal feature extractor; collate a number of adjacent eigenvectors into an aggregate; compute generalized eigenvalues over the aggregate; compute at least one eigenvector corresponding to at least one highest value eigenvalue of the aggregate and assign those eigenvectors to a second layer of the audio feature extractor. - View Dependent Claims (31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42)
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Specification