Latent Space Vector Matching for Road Noise Cancellation

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Solution Overview

Problem

Conventional noise cancellation systems in vehicles face latency issues when transitioning between different road conditions, leading to suboptimal noise suppression.

Innovation Solution

The system employs an encoder to compress current reference signals into a lower-dimensional latent space vector, which is then matched to a cluster group in a database representing previously encountered conditions. This allows for quick retrieval and loading of corresponding configuration properties to generate an anti-noise signal.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional noise cancellation techniques are used, then noise suppression is achieved, but latency increases when transitioning between different road conditions

Engineering Contradiction:

Improvenoise suppression performanceVSAvoidlatency during condition transitions

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system pre-processes reference signals into compressed latent space representations and pre-identifies different road condition clusters during normal operation. When a transition occurs, the system has already prepared the compressed representations and cluster assignments, enabling rapid retrieval and adaptation without recalculating from raw signals, thus reducing latency while maintaining noise suppression reliability

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system transforms the high-dimensional reference signals into a compressed latent space with reduced dimensions. This parameter transformation allows the system to represent road conditions more efficiently and match them to pre-defined clusters faster, reducing the computational time during transitions while preserving the essential characteristics needed for noise suppression

Inventive Principle:

Principle #35Parameter changes

2Productivity

If reference signals are compressed to latent space, then processing speed increases, but information loss may occur

Engineering Contradiction:

Improveprocessing speedVSAvoidsignal information during compression

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The system extracts only the most essential features from the high-dimensional reference signals and stores them in a compressed latent space representation. By selectively extracting critical signal characteristics rather than preserving all original data, the system achieves faster processing while minimizing information loss that would affect noise cancellation performance

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250069581A1Learning road condition representation for active road noise cancellation

Publication Date: 2025.02.27 ANALOG DEVICES INC

AI summary

Active noise cancellation techniques use an encoder to compress current reference conditions to a lower-dimensional latent space vector. The techniques also store a database of latent space vectors, which are representative of previously encountered reference conditions, and associated configuration parameters, such as filter coefficients/taps. Hence, when a vehicle transitions to a different condition (e.g., road condition) from a current condition, the system can match it with a previously encountered condition and quickly load corresponding configuration parameters for active noise cancellation.