Podcast Repetitive Content Detection via Multi-Modal Fusion
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Solution Overview
Problem
Existing systems for detecting repetitive content in podcasts are either computationally expensive and require large training datasets or rely solely on conventional fingerprinting techniques, which can miss forms of repetitive content, especially when dealing with overlapping speech, music, or multiple media types.
Innovation Solution
A system that combines text matching, audio feature matching, and fingerprint matching processes to detect repetitive content in podcasts, operating in both text and audio domains, and consolidates outputs to confidently identify repetitive segments and timestamps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fingerprinting techniques are used for repetitive content detection, then the system is simple to implement, but it misses forms of repetitive content especially when dealing with overlapping speech, music, or multiple media types
Solution Approach 1:
The patent combines three distinct detection approaches (fingerprint matching, audio feature matching, and text matching) into a unified system. Each method targets different types of repetitive content: fingerprint matching for exact audio segments, audio feature matching for overlapping speech and music, and text matching for spoken word repetitions. The consolidation module integrates results from all three methods to comprehensively identify repetitive content that would be missed by any single approach.
2Reliability
If multiple detection methods (text matching, audio feature matching, fingerprint matching) are combined, then detection accuracy improves, but computational cost increases
Solution Approach 1:
The patent divides the podcast content into multiple time windows and processes each window independently through the three detection methods. This segmentation allows parallel processing of different segments, reducing the overall computational burden. The consolidation module then aggregates results across segments, enabling efficient detection of repetitive content throughout the entire podcast without requiring exhaustive analysis of the complete audio stream at once.
Solution Approach 2:
The system applies detection methods selectively rather than uniformly across all content. The consolidation module determines whether repetitive content is present based on results from the three methods, applying further processing only when necessary. This partial action approach reduces computational waste by avoiding unnecessary analysis of segments that clearly do not contain repetitive content.
3Measurement precision
If existing systems require large training datasets, then detection models become more accurate, but the system becomes computationally expensive and difficult to deploy
Solution Approach 1:
The system uses reference fingerprint data, reference audio features, and reference text sentences that can be extracted from the podcast content itself or from a database of known repetitive content. This self-service approach eliminates the need for extensive external training datasets, as the system generates its own reference materials from the content it analyzes, making deployment simpler and more accessible.
Data Source
AI summary
In one aspect, a method includes detecting a fingerprint match between query fingerprint data representing at least one audio segment within podcast content and reference fingerprint data representing known repetitive content within other podcast content, detecting a feature match between a set of audio features across multiple time-windows of the podcast content, and detecting a text match between at least one query text sentences from a transcript of the podcast content and reference text sentences, the reference text sentences comprising text sentences from the known repetitive content within the other podcast content. The method also includes responsive to the detections, generating sets of labels identifying potential repetitive content within the podcast content. The method also includes selecting, from the sets of labels, a consolidated set of labels identifying segments of repetitive content within the podcast content, and responsive to selecting the consolidated set of labels, performing an action.