Device Control Through Audio Transition Detection and Fingerprint Switching
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Solution Overview
Problem
Existing streaming content technologies face challenges in conserving device resources such as CPU processing power, battery life, memory, and network bandwidth, especially when recognizing and synchronizing audio and video content across multiple devices, leading to limited interactivity and increased costs.
Innovation Solution
A system utilizing a classifier, transition detector, and context manager to recognize audio and video transitions, optimizing resource usage through contextual classification and digital fingerprinting, allowing seamless synchronization across devices without direct physical connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous audio and video content recognition is performed to enable interactivity, then content recognition accuracy is improved, but device resource consumption (CPU power, battery life, memory, network bandwidth) increases
Solution Approach 1:
The system performs preliminary classification of audio and video content into distinct categories (e.g., music, speech, silence) before triggering full content recognition. This preliminary action filters out unnecessary processing for continuous content, reserving full recognition only for transitional segments where interactivity is most valuable, thereby reducing overall device resource consumption while maintaining recognition accuracy for critical moments
Solution Approach 2:
Instead of continuous full-processing, the system employs periodic sampling and classification at different rates depending on content type. Audio and video streams are analyzed at periodic intervals to detect transitions, with processing intensity adjusted based on current content characteristics. This periodic action maintains recognition capability while significantly reducing average resource consumption compared to continuous full-processing
2Ease of operation
If audio and video content is synchronized across multiple devices to enhance interactivity, then user experience is improved, but network bandwidth and device resources are consumed
Solution Approach 1:
The system extracts only the essential synchronization information (transition timing, content type, duration) from the full audio and video streams and transmits these condensed metadata packets across devices. This extraction approach eliminates the need to transmit complete media streams for synchronization, dramatically reducing network bandwidth requirements while enabling multi-device coordination and enhanced user experience
Solution Approach 2:
A centralized content management server acts as an intermediary that receives audio and video streams from content sources, performs classification and transition detection, then distributes processed synchronization data to multiple client devices. This intermediary approach centralizes the resource-intensive processing work, allowing client devices to receive pre-processed synchronization information without consuming their own network bandwidth for full stream transmission
3Measurement precision
If transition detection algorithms are enhanced to recognize subtle audio and video changes, then content identification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The transition detection algorithm is segmented into multiple independent analysis stages: audio feature extraction, video frame analysis, transition pattern matching, and contextual verification. Each stage processes specific aspects of the media stream in parallel, allowing the system to maintain high detection accuracy through comprehensive multi-stage analysis while reducing total processing time by dividing the complex task into manageable concurrent operations rather than sequential processing
Data Source
AI summary
Apparatus, methods, and systems that operate to provide interactive streaming content identification and processing are disclosed. An example apparatus includes a classifier to determine an audio characteristic value representative of an audio characteristic in audio; a transition detector to detect a transition between a first category and a second category by comparing the audio characteristic value to a threshold value among a set of threshold values, the set of threshold values corresponding to the first category and the second category; and a context manager to control a device to switch from a first fingerprinting algorithm to a second fingerprinting algorithm different than the first fingerprinting algorithm, responsive to the detected transition between the first category and the second category.