Episode Identification via Audio Fingerprint and Sequential Pattern Analysis
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Solution Overview
Problem
Existing media monitoring systems struggle to accurately identify episode numbers of television shows, especially for episodes without reference data, due to the lack of efficient methods for fingerprint matching and sequential pattern recognition across multiple viewing locations.
Innovation Solution
The system employs fingerprint matching across episodes to identify the 'bumper' section, which is consistent across episodes of a series. By analyzing meter data from multiple viewing locations, the system extracts episode fingerprints and labels them based on sequential viewing patterns, thereby identifying previously unidentifiable media episodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional media monitoring methods are used to identify episodes, then known episodes can be identified, but episodes without reference data cannot be accurately identified
Solution Approach 1:
The system performs preliminary fingerprint extraction from the media content and stores it in a database before identification is needed. When an episode needs to be identified, the extracted fingerprint is compared against the pre-stored fingerprints in the database, enabling rapid and accurate identification even for episodes without traditional reference data.
Solution Approach 2:
The patent introduces audio fingerprints as an intermediary element between the media content and the identification system. Instead of directly comparing entire episodes or relying on metadata, the system extracts and compares compact audio fingerprint representations, which serve as a reliable mediator for identifying episodes across different viewing locations and devices.
2Loss of time
If manual episode identification methods are used, then some episodes can be identified, but the process is time-consuming and delays media reports
Solution Approach 1:
The patent replaces manual mechanical identification processes with automated electronic fingerprint matching. The system automatically extracts audio fingerprints from media content, compares them against the database using computational algorithms, and generates identification results without human intervention, dramatically reducing the time required for episode identification and report generation.
Solution Approach 2:
The system is designed to autonomously perform episode identification without requiring manual input or intervention. The fingerprint extraction, database comparison, and episode labeling processes occur automatically, allowing the system to serve itself in identifying episodes and generating reports, thereby eliminating delays associated with manual processing.
3Measurement precision
If comprehensive fingerprint matching is performed across all episodes, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent extracts only the essential identifying features from the media content to create compact audio fingerprints. Instead of analyzing and comparing entire episodes or large portions of content, the system isolates and processes only the fingerprint-relevant audio characteristics, significantly reducing computational complexity while maintaining high identification accuracy.
Solution Approach 2:
The system transforms the complex media content into simplified fingerprint parameters that capture the essential identifying characteristics. By changing the representation from raw audio data to compressed fingerprint parameters, the system reduces the dimensionality and complexity of the matching process while preserving the ability to accurately distinguish between different episodes.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to identify an episode number based on fingerprint and matched viewing information are disclosed. An example method includes processing meter data to identify a presented media based on a bumper included in a media, filtering the meter data based on the identification of the media, selecting a candidate episode, the candidate episode not associated with a known episode label, determining whether the candidate episode appears sequentially after a known episode for a threshold number of presentation locations, and labeling the candidate episode as the next sequential episode after the known episode in response to determining that the candidate episode appears sequentially after the known episode for the threshold number of presentation locations.