Wind Noise Suppression for Vehicle Voice Interfaces
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Solution Overview
Problem
Wind noise interferes with voice-based user interfaces in vehicles, leading to poor performance in automatic speech recognition systems, causing errors in voice command recognition and intent detection, especially in noisy environments like cars.
Innovation Solution
An apparatus and method that utilize a microphone array and wind detector to assess wind noise levels, enabling either on-device wind noise suppression or a combination of on-device suppression and in-cloud audio reconstruction to mitigate wind noise effects, improving voice command recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If wind noise suppression processing is performed on audio input, then wind noise interference is reduced, but device complexity increases
Solution Approach 1:
The audio processing system is segmented into multiple functional modules: a wind detector that analyzes audio signals to determine wind noise levels, and a processor that selectively applies wind noise suppression algorithms based on the detected wind noise level. This segmentation allows the system to handle different noise conditions independently rather than continuously processing all audio through complex suppression algorithms.
Solution Approach 2:
The system dynamically adjusts its processing complexity based on real-time wind noise detection. When wind noise levels exceed a threshold, the processor activates enhanced wind noise suppression; when wind noise is minimal, the system uses standard processing. This dynamic adaptation optimizes the balance between noise suppression effectiveness and computational resource utilization.
2Measurement precision
If advanced wind noise suppression and audio reconstruction are implemented, then voice command recognition accuracy improves, but use of energy increases
Solution Approach 1:
The system applies partial processing by selectively enabling advanced wind noise suppression and audio reconstruction only when wind noise levels exceed a determined threshold. Instead of continuously applying these computationally intensive algorithms to all audio input, the system activates them only when necessary, reducing overall energy consumption while maintaining high recognition accuracy during windy conditions.
Solution Approach 2:
The processor changes operational parameters based on wind noise level detection. When wind noise is detected above a threshold, the system adjusts processing parameters to enable enhanced suppression algorithms and audio reconstruction; when wind noise is below the threshold, parameters are adjusted to use standard processing modes, thereby optimizing energy efficiency across different operating conditions.
3Speed
If continuous audio monitoring for wake-word detection is performed, then device responsiveness improves, but false activation due to wind noise increases
Solution Approach 1:
The wind detector continuously monitors audio input and provides feedback about wind noise levels to the processor. This feedback mechanism allows the system to distinguish between genuine wake-words and wind noise-induced false activations. When wind noise levels are high, the system can adjust its wake-word detection sensitivity or require additional confirmation, reducing false activations while maintaining responsiveness to actual user commands.
Data Source
AI summary
Apparatus, methods and computer-readable medium are provided for processing wind noise. Audio input is processed by receiving an audio input. A wind noise level representative of a wind noise at the microphone array is measured using the audio input and a determination is made, based on the wind noise level, whether to perform either (i) a wind noise suppression process on the audio input on-device, or (ii) the wind noise suppression process on the audio input on-device and an audio reconstruction process in-cloud.