Audio Identification System with Predictive Caching for Battery Efficiency
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Solution Overview
Problem
Existing audio identification systems are resource-intensive for battery-powered devices, consuming significant battery power and incurring high costs due to processing and data transmission, and also face computational load challenges on servers during database lookups.
Innovation Solution
Implementing predictive caching of audio fingerprint sequences and corresponding identifiers on both devices and servers, allowing for continuous monitoring and identification of audio items by switching between watching and tracking modes, reducing the need for frequent database searches and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If audio identification is performed using traditional database lookup methods, then identification accuracy is maintained, but battery power consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by extracting and caching audio fingerprints locally on the device before actual identification is needed. This allows the device to maintain identification capability without continuously consuming battery power for full database lookups, resolving the contradiction between maintaining accuracy and reducing energy consumption.
Solution Approach 2:
The patent creates a local copy of audio fingerprints and metadata in a cache storage mechanism. This copy allows the device to perform identification using stored reference data rather than continuously querying the remote database, significantly reducing battery consumption while maintaining identification accuracy.
2Productivity
If continuous audio monitoring is implemented, then identification responsiveness improves, but computational load on servers increases
Solution Approach 1:
The system extracts only the essential audio fingerprints and metadata from continuous audio streams for local caching, while leaving the full audio data and complex processing tasks on the server. This extraction allows continuous monitoring with minimal server computational load, as only compact fingerprint data needs to be managed locally.
Solution Approach 2:
The patent segments the audio identification process into two parts: local fingerprint extraction and caching (light computational task), and remote database lookup only when necessary (heavy computational task). This segmentation allows continuous monitoring to proceed locally while keeping server computational load manageable.
3Reliability
If frequent database lookups are performed, then identification accuracy is maintained, but data transmission costs increase
Solution Approach 1:
The system performs preliminary fingerprint extraction and caching actions locally, so that when identification is needed, the device can use pre-fetched reference data instead of initiating new database lookups. This preliminary action reduces the frequency of data transmission while maintaining identification accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where identification results and updated fingerprints are fed back into the local cache. This feedback loop allows the system to learn from previous identifications and reduce future transmission needs, as the cache is continuously updated with relevant reference data without requiring constant server communication.
Data Source
AI summary
The present invention relates to providing identification information in response to an audio segment using a first mode of operation including receiving an audio segment and sending the audio segment to a remote server and receiving, from the remote server, identification information relating to the audio segment, and a second mode of operation of receiving an audio segment and using stored information to obtain identification information relating to the received audio segment received, without sending the audio segment to the remote server. The present invention further includes using identification information from the remote server and using local identification information and selecting either identification information from the remote server or local identification information based on selection criteria, and generating an output based on the selected identification information.