Earbud Location Detection via Acoustical Signature Customization
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Solution Overview
Problem
Conventional earbud proximity sensors often inaccurately determine the in-ear status, leading to erroneous decisions by host devices regarding playback, which wastes battery power and is not user-specific.
Innovation Solution
The use of acoustical signature-based detection with user-specific customization, where a non-user-specific machine learning model is trained and customized using user-specific in-ear samples to accurately determine the earbud's location, distinguishing between in-ear and out-of-ear states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional proximity sensors are used to determine in-ear status, then the device complexity is low, but the measurement precision is poor leading to inaccurate location detection
Solution Approach 1:
The patent replaces mechanical proximity sensors with an acoustical detection system that uses sound wave analysis. The earbud emits acoustic signals and analyzes the reflected sound patterns to determine in-ear status, substituting mechanical contact-based detection with acoustical field-based detection, thereby improving measurement precision without requiring complex mechanical structures
Solution Approach 2:
The system changes the detection parameter from physical proximity measurement to acoustical signature analysis. By analyzing parameters such as sound reflection patterns, frequency response, and acoustic impedance, the system achieves more precise in-ear detection that accounts for variations in ear canal geometry, effectively resolving the contradiction between simplicity and accuracy
2Adaptability or versatility
If non-user-specific machine learning models are used initially, then the device complexity is moderate, but the adaptability is poor for individual users
Solution Approach 1:
The system performs preliminary acoustical sampling during a calibration period when the earbud is worn by the user. It collects baseline acoustic data representing the user's unique ear canal characteristics, then uses this preliminary information to train and customize the machine learning model specifically for that user, enabling subsequent highly accurate and personalized detection
Solution Approach 2:
The earbud system automatically performs user-specific model customization without requiring external intervention or complex setup procedures. The system self-calibrates by collecting acoustic data during normal use and automatically training its own detection model, making the adaptability improvement transparent to the user while managing the complexity internally
3Measurement precision
If acoustical signature-based detection with user-specific customization is implemented, then the measurement precision and adaptability are improved, but the use of energy increases due to ML model training and customization
Solution Approach 1:
The system performs computationally intensive ML model training and customization only periodically during initial calibration or when triggered by specific events, rather than continuously. During normal operation, the pre-trained model performs lightweight inference with minimal energy consumption, thereby achieving high precision detection while managing overall energy usage effectively
Solution Approach 2:
The system performs model customization in advance during a dedicated calibration period when the user is likely to have the earbuds in use anyway. By completing the energy-intensive training work beforehand, the system avoids repeated high-energy operations during subsequent detection tasks, reducing overall energy consumption while maintaining high accuracy
Data Source
AI summary
An earbud is configured to detect its location (e.g., in-ear and out-of-ear) based on an acoustical signature with and without user-specific customization. The earbud location may be indicated to a host, e.g., to determine playback. Location determinations are based on features extracted from acoustical samples taken by the earbud compared to features extracted from out-of-ear acoustical samples and non-user-specific and/or user-specific in-ear samples. A non-user-specific machine learning (ML) model trained on features extracted from non-user-specific in-ear and out-of-ear samples may be an initial/default locator. The non-user-specific model may be customized for specific users. A user-specific in-model may be created by training the non-user-specific model on features extracted from user-specific in-ear samples collected when the earbud is located in-ear for a specific user. The user-specific ML model may be selected to classify a location of the earbud for one or more associated hosts.