Apple Inc.

Motorola Corporation

Rutgers, The State University of New Jersey

Adam B Fineberg

Hsiao-Wuen Hon

Richard James Mammone

Joseph Wilder

Peter V De Souza

Yen-Lu Chow

Peter Vincent deSouza

Stephen C Austin

William E Mayo

Mark C Croft

Zwi Kalman

Baosheng Yuan

Energy dispersive x-ray diffraction spectra are obtained from numerous volume elements within an object. A feature set such as a set of cepstrum coefficients is extracted from each spectrum and classified by a trained classifier such as a neural network to provide an indication of whether or not contraband such as explosives is present in the volume element. Indications for adjacent volume elements are evaluated in conjunction with one another, as by an erosion process, to suppress isolated indications and thereby suppress false alarms.

Analysis methods for energy dispersive X-ray diffraction patterns

Tone-sensitive acoustic models are generated by first generating acoustic vectors which represent the input data. The input data is separated into multiple frames and an acoustic vector is generated for each frame which represents the input data over its corresponding frame. A tone-sensitive parameter is then generated for each of the frames which indicates the tone of the input data at its corresponding frame. Tone-sensitive parameters are generated in accordance with two embodiments. First, a pitch detector may be used to calculate a pitch for each of the frames. If a pitch cannot be detected for a particular frame, then a pitch is created for that frame based on the pitch values of surrounding frames. Second, the cross covariance between the autocorrelation coefficients for each frame and its successive frame may be generated and used as the tone-sensitive parameter. Feature vectors are then created for each frame by appending the tone-sensitive parameter for a frame to the acoustic vector for the same frame. Then, using these feature vectors, acoustic models are created which represent the input data.

Method and apparatus for tone-sensitive acoustic modeling

A sound recognizer uses a feature value normalization process to substantially increase the accuracy of recognizing acoustic signals in noise. The sound recognizer includes a feature vector device which determines a number of feature values for a number of analysis frames, a min/max device which determines a minimum and maximum feature value for each of a number of frequency bands, a normalizer which normalizes each of the feature values with the minimum and maximum feature values resulting in normalized feature vectors, and a comparator which compares the normalized feature vectors with template feature vectors to identify one of the template feature vectors that most resembles the normalized feature vectors.

Method and recognizer for recognizing a sampled sound signal in noise

A tonal sound recognizer determines tones in a tonal language without the use of voicing recognizers or peak picking rules. The tonal sound recognizer computes feature vectors for a number of segments of a sampled tonal sound signal in a feature vector computing device, compares the feature vectors of a first of the segments with the feature vectors of another segment in a cross-correlator to determine a trend of a movement of a tone of the sampled tonal sound signal, and uses the trend as an input to a word recognizer to determine a word or part of a word of the sampled tonal sound signal.

Method and recognizer for recognizing tonal acoustic sound signals

A pattern recognition system which continuously adapts reference patterns to more effectively recognize input data from a given source. The input data is converted to a set or series of observed vectors and is compared to a set of Markov Models. The closest matching Model is determined and is recognized as being the input data. Reference vectors which are associated with the selected Model are compared to the observed vectors and updated ('adapted') to better represent or match the observed vectors. This updating method retains the value of these observed vectors in a set of accumulation vectors in order to base future adaptations on a broader data set. When updating, the system also may factor in the values corresponding to neighboring reference vectors that are acoustically similar if the data set from the single reference vector is insufficient for an accurate calculation. Every reference vector is updated after every input; thus reference vectors neighboring an updated reference vector may also be updated. The updated reference vectors are then stored by the computer system for use in recognizing subsequent inputs.

Continuous reference adaptation in a pattern recognition system

A method for correcting cepstral vectors representative of speech generated in a test environment by use of a vector quantization (VQ) system with a codebook of vectors that was generated using speech and acoustic data from a different (training) environment. The method uses a two-step correction to produce test environment cepstral vectors with reduced non-speech acoustic content. The first correction step subtracts, from the test vector, a coarse correction vector that is computed from an average of test environment cepstral vectors. The second step involves a VQ of the coarsely corrected test vector at each node of the VQ tree. The third step is the addition of a fine correction vector to the coarsely corrected test vector that is generated by subtracting a running (moving) average of the coarsely corrected test vectors associated with the deepest VQ tree node from the VQ vector closest to the coarsely corrected test vector. The method is independent of the means used to generate the cepstral vectors and the corrected output cepstra vectors may be used in various speech processing and classifying systems. The method is adaptable to non-stationary environments.

Growing community of inventors

Saratoga, CA, United States of America

Adam B Fineberg