Adaptive Cross-Correlation for Vehicle Audio Direction
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Solution Overview
Problem
Current techniques for estimating the direction of arrival of audio events in vehicles are limited in accuracy and effectiveness, particularly in noisy environments and varying conditions, which can impact the vehicle's ability to safely navigate and respond to audio cues.
Innovation Solution
The implementation of a new cross-correlation phase transformation algorithm that adjusts the parameter p to balance between the GCC and GCC-PHAT algorithms, allowing for improved cross-correlation peak sharpness and noise resilience, enabling more accurate direction of arrival estimation using audio data from multiple sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cross-correlation algorithms (GCC or GCC-PHAT) are used for direction of arrival estimation, then the system can process audio data from multiple sensors, but the accuracy deteriorates in noisy environments and varying conditions
Solution Approach 1:
The patent implements a dynamic parameter p that adapts based on environmental conditions. The system continuously adjusts the parameter value between 0 and 1 depending on noise levels and signal characteristics, transitioning from static GCC (p=0) or GCC-PHAT (p=1) to a flexible hybrid approach that optimizes performance for current acoustic conditions
Solution Approach 2:
The patent modifies the cross-correlation algorithm by introducing a controllable parameter p that changes the weighting between different correlation methods. By adjusting this parameter based on signal-to-noise ratio and other environmental metrics, the system adapts its processing characteristics to maintain accuracy across varying acoustic conditions
2Measurement precision
If the cross-correlation algorithm is made more complex to improve accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
Rather than creating entirely new complex algorithms, the patent achieves improved accuracy by adjusting a single parameter p within an existing cross-correlation framework. This parameter controls the balance between different correlation approaches, providing enhanced performance through simple parameter modulation rather than structural complexity
Solution Approach 2:
The patent creates a unified cross-correlation algorithm that encompasses both GCC and GCC-PHAT as special cases (when p=0 and p=1 respectively). This universal algorithm handles multiple operating conditions with a single framework, eliminating the need for separate algorithm implementations and reducing overall system complexity
Data Source
AI summary
Techniques for adaptive cross-correlation are discussed. A first signal is received from a first audio sensor associated with a vehicle and a second signal is received from a second audio sensor associated with the vehicle. Techniques may include determining, based at least in part on the first signal, a first transformed signal in a frequency domain. Additionally, the techniques include determining, based at least in part on the second signal, a second transformed signal in the frequency domain. A parameter can be determined based at least in part on a characteristic associated with at least one of the vehicle, an environment proximate the vehicle, or one or more of the first or second signal. Cross-correlation data can be determined based at least in part on one or more of the first transformed signal, the second transformed signal, or the parameter.