Vehicle Audio Volume Control Using Passenger Voice Detection
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Solution Overview
Problem
Passengers frequently need to adjust the audio volume in vehicles to facilitate conversation, leading to distraction and safety risks due to repeated volume adjustments.
Innovation Solution
A machine-learning model trained on audio samples to detect passenger voice, dynamically adjusting the sound volume of vehicle loudspeakers based on the presence or absence of passenger voice, using convolutional and recurrent neural networks to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If passengers manually adjust audio volume to facilitate conversation, then communication between passengers is improved, but driver distraction and safety risks increase
Solution Approach 1:
The system automatically detects passenger voice presence and adjusts audio volume without requiring manual intervention. The machine learning model continuously monitors audio samples from microphones, identifies when passengers are speaking, and autonomously controls the loudspeaker volume, making the system self-regulating and eliminating the need for driver interaction
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the machine learning model continuously analyzes audio input from microphones, detects passenger speech patterns, and dynamically adjusts volume based on detected speech presence. This real-time feedback loop ensures automatic adaptation to conversation needs without driver involvement
2Ease of operation
If audio volume is reduced for passenger conversation, then communication is improved, but audio playback quality deteriorates when no conversation is occurring
Solution Approach 1:
The system dynamically adjusts audio volume based on real-time detection of passenger speech. The machine learning model continuously monitors audio samples and changes volume levels adaptively - reducing volume only when speech is detected and maintaining normal levels when no conversation is occurring. This dynamic adjustment preserves audio quality during non-conversation periods while enabling communication when needed
3Measurement precision
If machine learning model continuously monitors audio samples, then voice detection accuracy is improved, but computational energy consumption increases
Solution Approach 1:
The system processes audio samples at optimized intervals rather than continuously analyzing every audio moment. The machine learning model is trained to efficiently process discrete audio samples and make quick classification decisions, reducing computational load while maintaining detection accuracy. This periodic processing approach balances precision requirements with energy conservation in the vehicle's computing system
Data Source
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AI summary
The disclosure notably relates to a computer-implemented method of machine-learning for use in a dynamical adjustment of a sound volume of one or more loudspeakers inside a vehicle. The method comprises obtaining a dataset of audio samples. Each audio sample having a label representing a presence or absence of passenger voice. The method also comprises training a machine-learning model based on the dataset. The machine-learning is configured to receive an input audio sample. The machine-learning is configured to output information relative to presence or absence of passenger voice in the input audio sample.