Noise Cancellation Technologies, Inc.

Itt Corporation

Other

Jeffrey N Denenberg

Thomas B Slack

David Claybaugh

Chuan-Chieh Tan

Michael L Gordon

Eldon W Ziegler, Jr

Paul Friedlander

Edward D Weinberger

John Hohman

Ralph Busch

Roy H Scott

Allen R Smith

Randy V Sabett

An active plus selective headset system for provision of active attenuation of broadband noise as well as speech filtering comprising a headset with reference microphones, residual microphones and speakers on each of a pair of open backed muffs and a controller means.

Active plus selective headset

Hands free noise canceling headset

Adaptive canceller filter module

An active noise control system for controlling multiple home appliances and including a power distribution network by which many appliances can be quieted by remotely processing noise signal information and forwarding noise cancelling signals to the appliances through the distribution network.

Active noise attenuated DSP Unit

A method for compressing and subsequently decompressing digital data communicated in an interactive computer network, the network designed to provide informational and transactional services to a very large population of users. The method features steps for compressing bytes of network data before transmission by substituting variable-length code words obtained from a fixed, look-up table, and, reconstituting the bytes using a fixed, decompression look-up table when the code words are received at the data reception site. In accordance with the invention, the compression and decompression look-up tables are statistically compiled by sampling the occurrence frequency of byte pairs in the network data stream, and where byte pairs are found to occur above a predetermined frequency, code words having lengths inversely related to the occurrence frequency are created for inclusion in the table so that a code word may be substituted for one byte of a pair when the other byte of the pair is found to precede it during compression, and the byte reconstituted from the code word using the decompression table when the code word is received at the reception site. Additionally, where a byte and its preceding byte constitute a pair not found within the pairs compression table, the method features steps for transmitting the byte compressed in accordance with a context-free encoding scheme, together with a suitable escape code word. Yet further, the method features steps for combining other compression and decompression procedures with the byte-pair compression and decompression to produce a compound compression scheme for the network data stream.

Data compression method for use in a computerized informational and

A ducted axial fan for large diameter ducts (11) which includes equidistantly spaced sensors (22,23) upstream and downstream of an axial fan and spaced actuators (24, 26) located around the periphery of said duct to cancel tonal noise caused by the air turbulence generated by the rotation of the fan.

Ducted axial fan

A digitally controlled analog cancellation system containing an analog noise cancellation circuit with a residual microphone (39) with a digital virtual earth controller (40) for optimizing variable parameters employed in the analog cancellation circuit.

Analog noise cancellation system using digital optimizing of variable

A wireless remote active noise canceling headset including residual microphones (35, 34) mounted on the headset (30) with speakers (32, 33) located adjacent to the residual microphones and an algorithm driven synchronous controller to operate said headset.

Remote siren headset

A method and system by which the number of samples used to characterize the noise cycle of an active noise cancellation system varies dynamically as a function of the fundamental operating frequency of the system.

Variable point sampling

A probabilistic learning element for performing task independent sequential pattern recognition. The element receives sequences of objects and outputs sequences of recognized states composed of objects. A plurality of memory elements are utilized to store received objects in sequence and for storing in context learned information including previously learned states, objects contained in previously learned states, positional information for each object in a learned state and other predetermined types of knowledge relating to previously learned states and objects contained therein. The element correlates sequences of received objects with learned information relating to previously learned states for providing conditional probabilities to possible sequences of recognized states. The most likely state sequence is determined and outputted as a recognized sequence when the element detects that a state has ended. The memory for storing learned information is a context organized memory including a plurality of tree structures having various types of information stored in nodes thereof with certain of the tree structures including at each node an attribute list referring to other tree structures whereby searching is facilitated and unnecessary searching eliminated. The element derives support coefficients relating to how much information was available when calculating conditional probabilities and support coefficients and conditional probabilities are combined to provide a rating of confidence. When the rating of confidence exceeds a predetermined level, the element is caused to store the outputted recognized state sequence as a learned state sequence with the memories storing various types of knowledge relating to the learned sequence of states.

Probabilistic learning element

A probabilistic learning element for performing task independent sequential pattern recognition employs context driven searching. The element receives sequences of objects and outputs sequences of recognized states composed of objects. The element includes a short term memory for storing received objects in sequential context and long term memories for storing in sequential context previously learned states and predetermined types of knowledge relating to the previously learned states. The element correlates the information stored in the short term memory with information stored in the long term memories for assigning probabilities to possible next states in the sequence of recognized states. The correlation is facilitated by using the context of the information stored in the short term memory as a pointer to the context of the information stored in the long term memories. Based upon the probabilities of the possible next states the most likely next state is determined and outputted as a recognized next state in the recognized state sequence when the element determines that a state has ended. The element additionally includes means for providing a rating of confidence in the recognized next state. The ratings of confidence for a sequence of recognized states are accumulated and if the accumulated value exceeds a predetermined threshold level the element will be caused to store the recognized state sequence as a learned state sequence.

Probabilistic learning element employing context drive searching

A probabilistic learning system of the type that receives sequential input data and outputs sequences of recognized patterns. The system includes an array of interconnected probabilistic learning elements of the type that receive sequences of objects and outputs sequences of recognized states, the array of learning elements being interconnected to have a number of input learning elements and a number of output learning elements. The input data is partitioned between the input learning elements of the array so that the partitioned input data forms objects provided to the learning elements in an overlapping and redundant manner. The output sequences of recognized states from the output learning elements are collected and combined to provide a sequence of recognized patterns as an output of the probabilistic learning system. The reliability of the learning system is enhanced due to the overlapping and redundant nature in which the input objects are processed through the system and the time required to perform the system task is reduced through the use of parallel processing through the array. Each element of the array provides a signal correspoding to a rating of confidence in the recognized states and this rating of confidence is fed back to the input of the element to cause the element to learn the recognized states when the rating of confidence exceeds a predetermined threshold level. The rating of confidence is also provided to the inputs of prior elements in the array to cause the prior elements to learn their recognized states when the rating of confidence exceeds the predetermined threshold.

Probabilistic learning system

A probabilistic learning element particularly adapted for use as a task independent sequential pattern recognition device receives sequences of objects and outputs sequences of recognized states composed of objects and includes a plurality of memories for storing the received sequences of objects and previously learned states as well as predetermined types of knowledge relating to previously learned states. The sequences of received objects are correlated with the information relating to the previously learned states in order to assign probabilities to possible next states in the sequence of recognized states. Based upon the probabilities of the possible next states the most likely next state is determined and outputted as a recognized next state in the recognized state sequence when the element determines that a state has ended. The element additionally includes means for providing a rating of confidence in the recognized next state. The ratings of confidence for a sequence of recognized stated are accumulated and if the accumulated value exceeds a predetermined threshold level the element will be caused to store the recognized state sequence as a learned state sequence.

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Trumbull, CT, United States of America

Jeffrey N Denenberg