Abstract: A process and apparatus for solving the product y=Rw, where R is a matrix and w is a vector. The process includes a steps of using a matrix outer product structure of R to determine all of the unique entries in R and storing the unique monomials. A different unique number is assigned to unique entries so that each unique entry has an associated number, and the associated numbers are stored. Rw is then solved using the stored associated numbers to obtain a result in terms of the associated numbers, and converting the result to entries from the matrix R. In the preferred embodiment, the process is used for iterative training in a classification system and especially a classification system on a portable platform.

Abstract: Method and apparatus rejecting out-of-class inputs for pattern classification including a list of patterns with at least one in-context pattern and a rank tolerance. An input pattern is used to generate a classifier score for each pattern in the list and the classifier scores are ranked in decreasing order. The highest ranking score for an in-context pattern is determined and if the highest ranking score is 1 the in-context pattern is selected. If the highest ranking score is not 1 it is compared to the rank tolerance. When the highest ranking score is less than the rank tolerance the in-context pattern is selected and when the highest ranking score is greater than the rank tolerance the in-context pattern is rejected. Cohorts of the highest ranking pattern are removed from the list prior to the comparison.

Abstract: A system and method for determining operating parameters to control a communication rate for an adaptive rate communication system includes a transmitter (101) to transmit a signal (135) through a network (142) to a receiver (103). The receiver (103) determines operating parameters based on, among other things, packet information from a received signal (e.g., packet). The receiver conveys the operating parameters to the transmitter for use in subsequent communications from the transmitter (101) to the receiver (103).

Abstract: A TCS (200) and procedure (400) for identifying an unidentified class as a class of a group of classes includes a new tree-structured classifier (208) and training processor (204). Unidentified feature vectors representing an unidentified class are combined with predetermined models to compute a score for each of the unidentified feature vectors. Based on the scores for each of the unidentified feature vectors, an association is made with the predetermined models to identify the unidentified class. Predetermined models are created using a training procedure (300) for predetermined feature vectors associated therewith. A procedure (400) for identifying an unidentified class as a class of a group of classes is useful when determining access privileges to a device or system.

Abstract: An improved method and system for training and classifying using a low complexity and high accuracy multiresolutional polynomial classifier (412) is presented. A method of training an multiresolutional polynomial classifier which reduces the complexity of existing classifiers allows models representing subgroups of classes to easily be created. The models which represent subgroups of classes are applied to an unidentified input to produce a coarse classification of the unidentified input using a low order classifier. Once a coarse classification of the unidentified input is performed, a more detailed classification is performed using another low complexity classifier.

Abstract: A system and method for determining operating parameters to control a communication rate for an adaptive rate communication system includes a transmitter (101) to transmit a signal (135) through a channel (142) to a receiver (103). The receiver (103) determines operating parameters based on, among other things, an effective bit rate and a percentage of speech in the signal. The receiver conveys the operating parameters to the transmitter for use in subsequent communications from the transmitter (101) to the receiver (103).

Abstract: Classifiers (110) and a comparator (112) perform an identification method (400) to identify a class as one of a predetermined set of classes. The identification method is based on determining the observation costs associated with the unidentified class. The identification method includes combining models representing the predetermined set of classes and the unidentified vectors representing the class. The predetermined class associated with the largest observation cost is identified as the class. Additionally, a unique, low-complexity training method (300) includes creating the models which represent the predetermined set of classes.

Abstract: The present invention relates to a communications system (100) that is capable of adapting to an unknown or varying spectral environment in a channel (14) between two communications units (12, 16). The system (100) maintains a spectral profile of the channel (14) and uses the spectral profile to determine appropriate transmit parameters for the system (100). The system (100) can be programmed to provide an optimal transmit signal for achieving a predetermined performance goal (such as, for example, maximum data rate at a given bit error rate (BER)) in light of the spectral environment in the channel (14). In one embodiment, the spectral profile is maintained in a spectrum table memory (27) that is periodically (or continuously) updated.

Abstract: Classifiers (110) and a selector (112) perform an identification method (300) to identify an ordered set of vectors (e.g., spoken commands, phoneme identification, radio signatures, communication channels, etc.) representing a class as one of a predetermined set of classes. Training processor (104) performs a training method (200) to train a set of models and store the models in a model memory (108). Classifiers (110) receive models from the model memory and combine the models with the ordered set of vectors to determine a set of scores. The selector associates the set of scores with the predetermined set of classes to identify the ordered set of vectors as a class from the predetermined set of classes.

Abstract: An improved method of training a SISRS uses less processing and memory resources by operating on vectors instead of matrices which represent spoken commands. Memory requirements are linearly proportional to the number of spoken commands for storing each command model. A spoken command is identified from the set of spoken commands by a command recognition procedure (200). The command recognition procedure (200) includes sampling the speaker's speech, deriving cepstral coefficients and delta-cepstral coefficients, and performing a polynomial expansion on cepstral coefficients. The identified spoken command is selected using the dot product of the command model data and the average command structure representing the unidentified spoken command.

Abstract: A system is disclosed for an adaptive rate voice system to provide improvements in coded operation over changing communication channel (50) conditions. This adaptive rate system efficiently determines optimal voice/channel coding rates , coding strategies and modulation/demodulation for optimum voice quality and intelligibility. A system state estimator (100), channel status estimator (110) and channel status monitor (120) provides feedback in the system to optimize the communication channel. The system maintains a continuous link despite changing channel conditions and minimizes delays through the system. Even though simple in design, it provides relatively low complexity and powerful channel coding operation. Operating conditions are thus extended for CDMA and portable communication systems. Voice intelligibility is preserved in extremely noisy or even hostile channel conditions.

Abstract: A processing system time compresses a voice message before transmission, and processing system time expands the message after reception. To process the message the processing systems perform at least one of: (a) randomizing the order of a sequence of samples form a silent portion of the message after reception thereof before blending the sequence with a last portion of the expanded message; (b) selecting the sequence of samples from the silent portion of the message after reception thereof, the sequence selected being poorly correlated with the last portion of the expanded message, before blending the sequence with the last portion of the message; and (c) compressing the dynamic range of the message before transmission, by an amount dependent upon the signal-to-noise ratio of the message, and aggressively expanding the dynamic range of the message after reception, by a fixed amount.

Abstract: A method and apparatus for training a system to assess the identity of a person through the audio characteristics of their voice. The system inserts an audio input (10) into an A/D Converter (20) for processing in a digital signal processor (30). The system then applies Neural network type processing by using a polynomial pattern classifier (60) for training the speaker recognition system.