Vehicle Recognition via Engine Sound Analysis
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Solution Overview
Problem
Enterprises face challenges in creating and maintaining user profiles due to data collection, storage, and sharing regulations, as well as security concerns, which hinder effective user acquisition activities.
Innovation Solution
A vehicle profiling system that generates user profiles by analyzing engine sound recordings using a machine learning model trained on engine sound data, including range and direction information, to identify vehicle models and distances, and subsequently aggregate this data to create customer profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If enterprises collect and store detailed user information to generate user profiles, then user acquisition effectiveness is improved, but data security risks and regulatory compliance difficulties increase
Solution Approach 1:
The patent creates audio fingerprints as simplified copies of vehicle engine sounds. Instead of storing and processing complete audio recordings or detailed vehicle information, the system extracts and stores only the essential acoustic signature (frequency spectrum, temporal patterns). This copy contains enough information for identification while being minimal in size and less sensitive, thereby improving user acquisition effectiveness without compromising data security
Solution Approach 2:
The patent replaces traditional mechanical/data-intensive profile creation methods with acoustic field-based identification. Instead of collecting detailed user information through forms, surveys, or device sensors, the system uses microphone-based audio capture and machine learning models to automatically generate profiles. This substitution reduces data collection burdens and security risks while maintaining profile generation capability
2Measurement precision
If enterprises collect comprehensive user data for profile generation, then profile accuracy is improved, but regulatory compliance difficulties and data loss risks increase
Solution Approach 1:
The system creates audio fingerprints as compact representations of vehicle characteristics. These fingerprints capture essential identification features (engine sound patterns, frequency spectra) while discarding redundant information. The machine learning models train on these compressed representations, achieving accurate vehicle identification and profile generation without requiring storage of complete audio datasets, thereby reducing data loss risks from theft or breaches
Solution Approach 2:
The patent extracts only the most relevant acoustic features from vehicle sounds for profile generation. The system identifies and isolates key characteristics such as fundamental frequency, harmonics, and temporal patterns, separating these essential elements from the rest of the audio signal. This extraction process creates minimal yet sufficient data sets that maintain profile accuracy while minimizing exposure to data loss risks
3Measurement precision
If sound data from multiple microphones is processed to improve vehicle detection accuracy, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
The patent divides the audio processing task into distinct segments: individual microphone capture, audio fingerprint extraction per microphone, and centralized comparison against the database. Each microphone independently generates audio fingerprints from its captured sound, which are then aggregated and compared. This segmentation allows parallel processing of multiple microphone inputs without requiring complex real-time coordination, thereby improving detection accuracy while managing system complexity
Solution Approach 2:
The patent introduces audio fingerprints as an intermediary representation between raw sound data and vehicle identification. Instead of directly processing and comparing complete audio signals from multiple microphones, the system first converts each signal into a compressed fingerprint representation. These intermediate fingerprints serve as simplified proxies that retain identification capability while reducing computational complexity, enabling accurate multi-microphone processing without overwhelming system resources
Data Source
AI summary
Methods and systems are described herein for generating vehicle profiles for detected vehicles based on engine sound recordings. Based on those vehicle profiles, an enterprise is enabled to generate a user profile for customers. To generate the vehicle profile, a vehicle profiling system may be used. The vehicle profiling system may receive sound data with range information. The vehicle profiling system may input the sound data into a trained machine learning model and receive vehicle model information for the sound data. Based on the output, the profiling system may retrieve metadata associated with the vehicle model and generate a profile based on the metadata.