Sound Signature Micro Context Recognition
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Solution Overview
Problem
Existing context-aware mobile applications rely on macro context data, which limits their ability to provide detailed user context information, such as specific activities or environments, as they do not effectively utilize sound signatures for more refined context recognition.
Innovation Solution
A method that extracts sound signatures from environment sound samples and logically associates them with macro context data to determine a more specific 'micro context' using pattern recognition techniques, including supervised and unsupervised learning approaches, allowing for the recognition and discovery of new sound and context patterns, even if initial data is unavailable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If macro context data from hardware and software sensors is used for context awareness, then basic location and device information can be obtained, but detailed user context information such as specific activities or environments cannot be identified
Solution Approach 1:
The patent combines macro context data from hardware and software sensors with sound signature extraction and analysis to create a comprehensive context recognition system. By merging multiple data sources (sensors, sound samples, pattern recognition), the system achieves detailed micro context identification without requiring a complete redesign of the device architecture.
Solution Approach 2:
The patent introduces sound signatures as an intermediary element that bridges the gap between basic macro context data and detailed micro context information. Sound signatures serve as a mediator that translates environmental acoustic characteristics into actionable context data, enabling detailed activity and environment recognition without direct complex sensing.
2Measurement precision
If sound signatures are extracted and analyzed to determine micro context, then detailed user context recognition is achieved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the essential sound signature features from complete sound samples, rather than analyzing entire audio recordings. This extraction approach isolates the critical acoustic characteristics needed for context recognition, reducing computational complexity while maintaining high measurement precision for micro context determination.
Solution Approach 2:
The patent performs preliminary sound signature extraction and macro context data collection before the actual context recognition process. By preparing and pre-processing the data in advance, the system reduces the computational burden during real-time analysis, enabling precise micro context recognition without excessive processing complexity during critical operations.
3Adaptability or versatility
If pattern recognition techniques including unsupervised learning are used to discover new sound patterns, then unknown sound signatures and micro contexts can be identified, but processing time and computational resources increase
Solution Approach 1:
The patent applies pattern recognition techniques selectively rather than continuously. By using unsupervised learning and anomaly detection only when needed (such as when new sound patterns are suspected or existing patterns are insufficient), the system achieves high adaptability for discovering unknown sound signatures while minimizing processing time for routine context recognition tasks.
4Reliability
If sound samples are collected and stored in a database for future reference, then context recognition accuracy improves, but device storage requirements and data management complexity increase
Solution Approach 1:
The patent extracts and stores only the essential sound signature features from complete sound samples, rather than storing entire audio recordings. This extraction approach significantly reduces the storage volume required while maintaining the reliability needed for accurate context recognition, as only the critical acoustic characteristics are preserved for future reference.
Data Source
AI summary
Method for user micro context recognition using sound signatures. The method includes: recording an environment sound sample from a microphone into a mobile device by a trigger stimulus; simultaneous to recording an environment sound sample, collecting hardware and software macro context data from available mobile devices; extracting a sound signature from the recorded sound sample based on sound features and spectrograms; comparing for similar patterns the recorded sound signature with reference sound signatures stored in a sound database; updating the sound database; checking if the recorded sound was recognized; performing a logical association between the sound label and the available macro context data; comparing for similar patterns the recorded context with a reference context stored in a context database; updating the context database; checking if the micro context was recognized; and returning to a mobile context-aware application the micro context label.