Vehicle Audio Adaptation Using Neural Network Feature Extraction
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Solution Overview
Problem
Conventional vehicle technologies fail to provide occupants with a sound that harmonizes with the external environment during driving, leading to a disconnect between the auditory and visual experiences of the natural environment.
Innovation Solution
A vehicle system comprising a camera, speaker, and controller that uses pre-trained neural networks to analyze external images and select sound samples that match the terrain and emotional information, allowing for real-time audio output that complements the driving environment, including sound effects and music with adjustable playback settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pre-trained neural network is used to analyze external images and select matching sound samples in real-time, then the adaptability to external environment is improved, but the device complexity increases
Solution Approach 1:
The neural network is pre-trained offline to learn the mapping between visual features of external environments and corresponding sound samples. This preliminary action transfers the computational burden from real-time vehicle operation to an offline training phase, enabling the system to quickly adapt to new environments without increasing real-time processing complexity
Solution Approach 2:
The system creates a simplified representation (feature vector) of the external environment by extracting key visual features through the neural network. This copied representation is then used to select appropriate sound samples, avoiding the need for complex real-time analysis of the entire external scene while maintaining adaptability
2Measurement precision
If the neural network extracts detailed terrain and emotional information from external images, then the sound selection accuracy is improved, but the processing time increases
Solution Approach 1:
The neural network extracts only the essential visual features (terrain type and emotional characteristics) from external images, rather than processing all visual information. This extraction approach maintains sound selection accuracy by focusing on the most relevant features while significantly reducing processing time through selective feature identification
3Adaptability or versatility
If multiple sound samples are selected and played with different playback settings, then the emotional comfort is improved, but the control complexity increases
Solution Approach 1:
Different playback settings (repetition count, playback time, fading effects) are applied locally to specific sound samples based on their type and the current emotional context. This allows the system to provide tailored audio experiences for different situations without requiring complex global control mechanisms, as each sound sample is processed according to its specific characteristics
Data Source
AI summary
An embodiment vehicle includes a camera, a speaker, and a controller electrically connected to the camera and the speaker, wherein the controller is configured to acquire a first external image outside the vehicle from the camera, input the first external image to a pre-trained first neural network and extract a first feature corresponding to the first external image, and control the speaker to output a first sound sample among a plurality of sound samples, based on a comparison of the first feature and pre-stored features corresponding to the plurality of sound samples.