Recursive Audio Fingerprint Search Tree Optimization
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Solution Overview
Problem
Audio fingerprinting systems face challenges in efficiently searching large sets of fingerprints, leading to increased search times and a high likelihood of false positives or false negatives, which affects real-time performance and accuracy.
Innovation Solution
The system organizes audio fingerprints into a recursive tree using a clustering algorithm based on similarity measures like Hamming or Euclidean distance, storing each fingerprint at multiple resolutions and performing hierarchical clustering to optimize search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If audio fingerprints are stored in a large database for comprehensive search coverage, then the completeness of fingerprint matching is improved, but the search time increases
Solution Approach 1:
The patent divides the large database of audio fingerprints into multiple clusters or groups based on similarity metrics. Each cluster contains fingerprints with similar characteristics, allowing the search algorithm to first identify relevant clusters and then perform detailed matching only within those clusters. This segmentation reduces the effective search space from the entire database to a manageable subset, thereby maintaining completeness while significantly reducing search time.
2Ease of manufacture
If traditional linear search methods are used to compare fingerprints, then the implementation simplicity is maintained, but the search efficiency deteriorates
Solution Approach 1:
The patent performs preliminary clustering of audio fingerprints into categories or groups based on their acoustic characteristics before the actual search process. This pre-organization creates a hierarchical structure where fingerprints are arranged in clusters that reflect their similarity. During search operations, the algorithm can quickly eliminate entire clusters that do not match the query, performing detailed comparisons only within relevant clusters. This preliminary action transforms the search process from a brute-force linear scan to a targeted exploration, dramatically improving efficiency while keeping the implementation relatively straightforward.
Data Source
AI summary
A system and method are presented for optimization of audio fingerprint search. In an embodiment, the audio fingerprints are organized into a recursive tree with different branches containing fingerprint sets that are dissimilar to each other. The tree is constructed using a clustering algorithm based on a similarity measure. The similarity measure may comprise a Hamming distance for a binary fingerprint or a Euclidean distance for continuous valued fingerprints. In another embodiment, each fingerprint is stored at a plurality of resolutions and clustering is performed hierarchically. The recognition of an incoming fingerprint begins from the root of the tree and proceeds down its branches until a match or mismatch is declared. In yet another embodiment, a fingerprint definition is generalized to include more detailed audio information than in the previous definition.