Adaptive Phase Discovery for Automotive Audio Noise Reduction
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Solution Overview
Problem
Audio signals in automotive environments are adversely impacted by acoustical and electrical characteristics, leading to poor sound quality in hands-free phone and voice recognition systems due to varying path lengths and environmental factors like reflections and diffractions.
Innovation Solution
An adaptive phase discovery system that estimates phase differences between audio signals across a broad frequency range using multiple microphones, filtering low and high-frequency phase differences to improve signal-to-noise ratios and enable accurate mixing and off-axis suppression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If phase differences are estimated across a broad frequency range using multiple microphones, then phase estimation accuracy is improved, but the system complexity increases
Solution Approach 1:
The frequency spectrum is segmented into different frequency ranges (low frequency and high frequency bands), allowing phase differences to be estimated separately for each band. This segmentation enables the system to handle complex broad-frequency phase estimation by breaking it down into manageable frequency-specific processing tasks, improving accuracy without overwhelming system complexity.
Solution Approach 2:
The system transitions from time-domain signal processing to frequency-domain processing by applying Fourier transforms. This dimensional change allows phase differences to be extracted and estimated across the frequency spectrum, enabling broad-frequency phase estimation while providing structured methods to manage the complexity through frequency-based organization.
2Reliability
If low and high frequency phase differences are filtered and adapted over time, then signal-to-noise ratio is improved, but processing time increases
Solution Approach 1:
The system applies preliminary filtering and adaptation operations to phase differences in the frequency domain before final signal reconstruction. By pre-processing phase information across frequency bands and adapting it over time, the system improves signal-to-noise ratio in advance, reducing the need for extensive post-processing and thereby managing overall processing time.
Solution Approach 2:
The phase difference estimation and adaptation is performed periodically over time frames, with low and high frequency components processed in alternating or interleaved manner. This periodic processing allows the system to maintain improved signal-to-noise ratio through continuous adaptation while controlling processing time by working in discrete time frames rather than continuously.
Data Source
AI summary
In an adaptive phase discovery system a first audio signal is received via a first microphone and a second signal is received via a second microphone. Corresponding audio frames of the first and second signals are each transformed into the frequency domain and a plurality of frequency sub-bands are generated. A phase is determined for each frequency sub-band in each signal. Instantaneous phase differences are determined between the signals at each of the frequency sub-bands. Lower frequency instantaneous phase differences are filtered over time to determine current phase differences at lower frequencies. When SNR is high in lower frequency sub-bands, lower frequency sub-band phase differences are tracked to the higher frequency sub-bands. The tracked higher frequency phase differences are filtered over time to determine phase differences for the current frame. The phase differences may be used to rotate phases in each sub-band and sum signals and/or to reject off-axis signals.