Local Audio Fingerprint Matching for Privacy and Energy Efficiency
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Solution Overview
Problem
Existing systems for identifying video programs to provide context-aware information are burdensome for users, consume excessive energy, and raise privacy concerns due to frequent data transmissions to servers for audio analysis.
Innovation Solution
A media server computes audio fingerprints for repeated segments of TV shows and sends them to client devices for local matching, reducing the need for continuous network connections and data transmission, while selecting a subset of relevant audio fingerprints based on user preferences and viewing history to minimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio data is transmitted frequently to the server for analysis, then the system can identify video programs accurately, but energy consumption increases and battery life decreases
Solution Approach 1:
The patent extracts only the essential audio fingerprint data from the complete audio stream, transmitting only these compressed representations to the server rather than the entire audio dataset. This extraction approach maintains identification accuracy while dramatically reducing the volume of transmitted data and associated energy consumption.
Solution Approach 2:
The system performs preliminary audio fingerprinting and local matching computations on the client device before transmitting results to the server. By preprocessing the audio data locally and only sending matching results or queries rather than continuous audio streams, the system reduces network traffic and server processing requirements, thereby lowering overall energy consumption.
2Measurement precision
If audio data is transmitted frequently to the server, then video program identification can be achieved, but user privacy is compromised
Solution Approach 1:
The system extracts only the essential audio fingerprint characteristics from the complete audio signal, transmitting only these abstract representations rather than the original audio content. This extraction preserves the ability to identify video programs while removing personally identifiable information and sensitive audio content, thereby protecting user privacy.
Solution Approach 2:
Audio fingerprints serve as an intermediary representation between the original audio content and the server analysis. These fingerprints act as a mediator that preserves identification capability while obscuring the actual audio content, allowing the server to perform matching without direct access to private user audio environments.
3Measurement precision
If all audio fingerprints are transmitted to the client device, then matching accuracy improves, but device memory and processing resources are excessive
Solution Approach 1:
The system tailors the audio fingerprint database on the client device to match the specific viewing habits and local context of each user. By customizing which fingerprints are stored locally based on user preferences, viewing history, and geographic location, the system optimizes matching accuracy for the user's specific situation while minimizing the overall quantity of stored data.
Solution Approach 2:
The system implements a two-tier matching approach where a subset of frequently accessed or most relevant audio fingerprints is stored locally for rapid matching, while less frequently used fingerprints remain on the server. This partial local storage provides sufficient matching accuracy for common scenarios without requiring the client device to store and process the complete fingerprint database.
4Speed
If continuous network connection is maintained for audio transmission, then real-time identification is achieved, but battery life is reduced
Solution Approach 1:
The system transitions from continuous network communication to periodic or event-driven transmission. Audio fingerprints are matched locally and results are transmitted only when needed (e.g., when a match is found, when the user queries for information, or at scheduled intervals), rather than maintaining a continuous open network connection. This periodic approach maintains real-time identification capability while dramatically reducing network power consumption.
Solution Approach 2:
The client device performs self-service by conducting local audio fingerprint matching against its stored database, eliminating the need for continuous server communication. The device independently identifies video programs by comparing captured audio against locally stored fingerprints, only contacting the server when additional fingerprints are needed or when results require verification, thereby reducing network dependency and power consumption.
Data Source
AI summary
A process adapts user-initiated search queries. The process executes at a client device with a microphone. The process downloads audio fingerprints from a remote server for a plurality of video programs, and downloads information that correlates the audio fingerprint to the video programs. The audio fingerprints are preselected according to relevancy criteria, including stored user preferences and prior search queries by the user. The audio fingerprints and correlating information are stored locally. The process detects ambient sound using the microphone and computes one or more sample audio fingerprints from the detected ambient sound. The process matches a sample audio fingerprint to a locally stored audio fingerprint and uses the correlating information to identify a first video program corresponding to the matched sample audio fingerprint. The process then receives user input to initiate a search query. The process provides auto-complete suggestions for the search query based on the first video program.