Personal Audio Privacy via Feature Extraction
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Solution Overview
Problem
Existing audio filtering technologies fail to effectively tailor ambient sound to individual preferences, particularly in varying environments, as they do not adequately address the need for frequency attenuation, amplification, noise reduction, and augmentation while ensuring user privacy and convenience.
Innovation Solution
A sound processing system comprising personal audio systems and a sound knowledgebase that uses collective feedforward methods to process ambient sound, selecting and applying appropriate filtering parameters based on location, ambient sound profiles, and user contexts, while ensuring privacy through feature extraction and metadata handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If audio filtering technologies are used to tailor ambient sound to individual preferences, then listening experience is improved, but user privacy is compromised due to collection of audio data
Solution Approach 1:
The patent extracts only the essential acoustic features (spectral centroid, spectral rolloff, spectral flux, zero-crossing rate, MFCCs) from the audio signal while discarding the raw audio data. This extraction approach maintains the ability to analyze and filter ambient sound according to user preferences while preventing reconstruction of the original audio content, thus protecting user privacy.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw audio data into feature representations. This intermediary step acts as a barrier between the original audio signal and storage/transmission, allowing the system to learn from audio patterns without directly accessing or storing sensitive audio content.
2Measurement precision
If ambient sound is processed and uploaded to remote devices for analysis, then filtering accuracy is improved, but data transmission requirements and processing time increase
Solution Approach 1:
The patent extracts only essential acoustic features (spectral centroid, spectral rolloff, spectral flux, zero-crossing rate, MFCCs) from the audio signal while discarding the raw audio data. This extraction approach maintains the ability to analyze and filter ambient sound according to user preferences while preventing reconstruction of the original audio content, thus protecting user privacy.
Solution Approach 2:
The patent performs feature extraction and preliminary analysis locally on the personal audio system before uploading to remote devices. This preliminary action reduces the data volume that needs to be transmitted and allows immediate local processing while still benefiting from remote collaborative learning.
3Adaptability or versatility
If collective feedforward methods are used to learn filtering parameters from multiple users, then adaptability to different environments is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal sound knowledge base that serves multiple functions: storing acoustic features from multiple users, learning filtering parameters through machine learning, and providing updated parameters to all connected personal audio systems. This multi-functional database reduces the need for each device to independently process and store environmental data.
Solution Approach 2:
The patent uses machine learning to create simplified representations (copies) of complex acoustic environments and user preferences. Instead of storing and processing all raw audio data from multiple users, the system learns filtering parameters that capture the essential patterns and applies these learned parameters across multiple devices.
Data Source
AI summary
Personal audio systems and methods are disclosed. A personal audio system includes a processor to generate a personal audio stream by processing an ambient audio stream in accordance with an active processing parameter set, a circular buffer memory to store a most recent snippet of the ambient audio stream, and an event detector to detect a trigger event. In response to detection of the trigger event, a controller may extract audio feature data from the most recent snippet of the ambient audio stream and transmit the audio feature data and associated metadata to a knowledgebase remote from the personal audio system.