Autonomously Motile Device Noise Suppression via Dynamic Beamforming
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Solution Overview
Problem
Autonomously motile devices face challenges in effectively suppressing noise from moving sources, leading to incorrect audio data processing and potential removal of desired audio signals during motion, due to difficulties in detecting relative motion between the device and noise sources.
Innovation Solution
A noise-suppression component utilizing a trained model with encoder and decoder architecture, including recurrent neural networks, processes audio data to determine high-level features and suppress noise, adapting to the motion of both the device and noise sources by retaining information over time to isolate desired audio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional noise suppression techniques are used in autonomously motile devices, then processing speed may be maintained, but noise suppression effectiveness deteriorates when the device is in relative motion with respect to the noise source
Solution Approach 1:
The beamforming technique is made adaptive by dynamically adjusting beamforming parameters based on detected noise characteristics and device motion state. The system transitions from static beamforming to dynamic beamforming that automatically adapts to changing relative motion conditions between the device and noise sources, resolving the contradiction between maintaining processing speed and improving noise suppression effectiveness during motion.
2Measurement precision
If beamforming is used to isolate audio from particular directions, then directional audio isolation is improved, but incorrect noise source identification occurs when the device moves relative to the noise source
Solution Approach 1:
The system employs feedback mechanisms where the detected state of relative motion and noise characteristics are fed back to adjust beamforming parameters in real-time. This closed-loop control ensures that the directional audio isolation remains accurate even when the device moves relative to noise sources, preventing incorrect noise source identification while maintaining measurement precision.
3Device complexity
If fixed beamforming parameters are used, then processing complexity is reduced, but noise suppression performance deteriorates during device motion
Solution Approach 1:
The system implements dynamic beamforming parameters that automatically adjust based on the device's motion state and noise environment. Rather than using fixed parameters, the beamforming configuration evolves dynamically to match changing conditions, improving noise suppression performance during motion while managing processing complexity through efficient adaptation algorithms.
Data Source
AI summary
A device capable of autonomous motion may move in an environment and may receive audio data from a microphone. A model may be trained to process the audio data to suppress noise from the audio data. The model may include an encoder that includes one or more convolutional layers, one or more recurrent layers, and a decoder that includes one or more convolutional layers.