Sensor-Array Processor Time-Domain Noise Suppression
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Solution Overview
Problem
Existing sensor array systems face challenges in efficiently processing signals in 3D environments due to noise and interference, particularly in applications like speech recognition, where high reverberation and multiple interference sources limit the effectiveness of automatic speech recognition (ASR) and other domains like sonar and radar, due to the complexity and latency associated with frequency domain processing.
Innovation Solution
An integrated sensor-array processor that operates in the time-domain with a sensor transform engine, spatial filter engine, noise reduction filter engine, and inverse transform engine, allowing for efficient suppression of noise and interference while maintaining low latency through flexible and efficient transform processing, including non-uniform frequency spacing and frame rates, and incorporating modules for source localization and acoustic echo cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency domain processing is used to improve filtering performance, then noise and interference suppression is improved, but latency and computational cost increase significantly
Solution Approach 1:
The patent segments the frequency domain processing into multiple overlapping blocks with different time delays. Each block processes a portion of the signal spectrum independently, allowing parallel computation. This segmentation enables the system to achieve frequency domain filtering performance while reducing overall latency through pipelining and overlapping processing of multiple blocks simultaneously.
Solution Approach 2:
The patent implements dynamic block selection and processing where the system can adaptively choose which blocks to process based on signal characteristics and latency requirements. The processing pipeline dynamically adjusts the number and size of blocks, enabling flexible trade-off between filtering performance and latency depending on real-time operational needs.
2Measurement precision
If frequency domain processing is used to improve noise suppression, then signal-to-noise ratio is improved, but computational cost increases significantly
Solution Approach 1:
The patent divides the frequency spectrum into multiple blocks and processes only the necessary portions of each block. By segmenting the processing into smaller manageable units, the system reduces the computational burden of full-spectrum frequency domain processing while maintaining effective noise suppression in the critical frequency ranges.
Solution Approach 2:
The patent applies partial frequency domain processing by selectively transforming and filtering only specific frequency blocks rather than processing the entire spectrum. This partial action approach achieves sufficient signal-to-noise ratio improvement for speech recognition applications without the excessive computational cost of complete frequency domain processing.
3Power
If ideal spatial filters are used to amplify target signals, then signal amplification is improved, but complete rejection of interference signals cannot be achieved due to fundamental limitations
Solution Approach 1:
The patent segments the spatial filtering problem by processing different spatial frequencies and directions in separate blocks. This allows the system to amplify target signals from specific directions while applying different filtering strategies for interference from other directions, achieving better overall interference rejection than single-stage ideal filters.
Solution Approach 2:
The patent changes the filtering parameters adaptively for different spatial frequencies and directions. By adjusting filter characteristics based on the specific spatial characteristics of target and interference signals, the system achieves improved signal amplification while simultaneously enhancing interference rejection beyond what fixed ideal filters can provide.
Data Source
AI summary
An integrated sensor-array processor and method includes sensor array time-domain input ports to receive sensor signals from time-domain sensors. A sensor transform engine (STE) creates sensor transform data from the sensor signals and applies sensor calibration adjustments. Transducer time-domain input ports receive time-domain transducer signals, and a transducer output transform engine (TTE) generates transducer output transform data from the transducer signals. A spatial filter engine (SFE) applies suppression coefficients to the sensor transform data, to suppress target signals received from noise locations and/or amplification locations. A blocking filter engine (BFE) applies subtraction coefficients to the sensor transform data, to subtract the target signals from the sensor transform data. A noise reduction filter engine (NRE) subtracts noise signals from the BFE output. An inverse transform engine (ITE) generates time-domain data from the NRE output.