ADAPTIVE ACOUSTIC ATTENUATION SYSTEM HAVING DISTRIBUTED PROCESSING AND SHARED STATE NODAL ARCHITECTURE

Patent №

US 5,963,651

Granted

1999-10-05

Filed 1997

Owner

DIGISONIX, INC.

+1 more

Lab

AI components

2

ml · hardware

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

08783426

An adaptive acoustic attenuation system has distributed nodal processing and a shared state nodal architecture. The system includes a plurality of adaptive filter nodes, each preferably having a dedicated digital signal processor. Each adaptive filter node preferably receives a reference signal and generates a correction signal that drives an acoustic actuator. Each adaptive filter node also shares nodal state vectors with adjacent adaptive filter nodes. The calculation of the nodal correction signals depends both on the reference signal and nodal state vectors received from adjacent adaptive filter nodes. The calculation of nodal state vectors shared with adjacent adaptive filter nodes depends on nodal state vectors received from other adjacent adaptive filter nodes as well as nodal reference signals inputting the adaptive filter node. Adaptation of adaptive weight vectors for generating the correction signals and adaptive weight matrices for generating nodal state signal vectors are adapted in accordance with globally transmitted error signals being back-propagated through the appropriate acoustic and electrical paths. The adaptive filter nodes can be arranged in a linear network topology, or in some other network topology such as but not limited to a random web network topology. The system allows the addition or elimination of additional reference signals and/or acoustic actuators with associated digital signal processing nodes to the system without requiring the system to be reconfigured and without requiring rewriting of software. The system is well-suited for high dimensional MIMO active acoustic attenuation systems.

Machine learningAI hardwareG10K 11/17855G10K 11/17854G10K 11/17881

AI classification

Machine learning1.00
AI hardware1.00
Evolutionary computation0.13
Vision0.02
Natural language0.01
Speech0.01
Knowledge representation0.00
Planning0.00

Ownership

DIGISONIX, INC.

assignment · 84400313

NELSON INDUSTRIES, INC.

assignment · 84400313

Assignors

VAN VEEN, BARRY D., LEBLOND, OLIVIER E., SEBALD, DANIEL J.

On an employer assignment, the assignors are typically the inventors.

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