Fingerprint Clustering for Audio Recognition Accuracy
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Solution Overview
Problem
Conventional content-based audio recognition methods face challenges in accurately identifying shared audio content due to the presence of repetitive sounds, which can lead to false positives by overrepresenting similar query fingerprints, thereby overshadowing other genuine matches.
Innovation Solution
A method that includes a fingerprint clustering phase to group similar query fingerprints together, using a trained neural network to generate fingerprints from time-frequency representations, and calculating matching scores to determine clustered groups, thereby minimizing the impact of repetitive sounds and improving decision-making accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional content-based audio recognition methods compare query fingerprints directly to reference fingerprints, then the process is simple and fast, but repetitive sounds cause false positives by overrepresenting similar query fingerprints
Solution Approach 1:
The patent segments the fingerprint matching process into distinct phases: generating query fingerprints, calculating matching scores against reference fingerprints, clustering similar query fingerprints together, and then making detection decisions based on clustered results. This segmentation isolates the repetitive sound problem to specific clusters, preventing false positives from propagating across all comparisons while maintaining systematic processing efficiency
Solution Approach 2:
The patent introduces clustering as an intermediary step between raw fingerprint matching and final detection decisions. This intermediary process groups similar query fingerprints and applies cluster-level filtering rules, acting as a mediator that prevents individual repetitive fingerprints from causing false positives while preserving genuine matches through controlled information flow
2Measurement precision
If all query fingerprints are compared to reference fingerprints without clustering, then processing is straightforward, but repetitive sounds overshadow genuine matches reducing detection accuracy
Solution Approach 1:
The patent performs preliminary clustering of query fingerprints before making final detection decisions. By pre-grouping similar fingerprints and identifying which clusters contain repetitive sounds, the system prepares filtered, high-quality candidate sets in advance, eliminating the need to process all individual fingerprints uniformly and reducing overall processing time while improving precision
Solution Approach 2:
The patent applies partial action by focusing computational resources only on non-repetitive fingerprint clusters that pass the cluster-level filtering rules. Instead of uniformly processing all query fingerprints, the system selectively intensifies analysis on promising clusters while efficiently dismissing repetitive ones, achieving high precision without excessive processing time across the entire fingerprint set
3Adaptability or versatility
If a large number of reference audio files are stored in the database, then the system can recognize more audio content, but the fingerprint matching phase becomes computationally intensive
Solution Approach 1:
The patent extracts and processes query fingerprints into clustered groups with aggregated matching scores before comparing against the large reference database. By extracting cluster-level representations rather than individual fingerprint comparisons, the system reduces the effective search space when querying a large reference database, maintaining versatility across millions of references while improving matching speed through dimensionality reduction
Data Source
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AI summary
Methods and a computer-readable storage device for detecting shared audio content between first audio content information and second audio content information is provided. The methods cluster similar fingerprints to lessen the impact that similar or repetitive sounds have on the final decision, thereby producing a more accurate final decision about whether the first audio content information shares audio content with the second audio content information.