Audio Fingerprinting With Sparse Vectors for Faster Matching
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Solution Overview
Problem
Existing audio identification systems face inefficiencies in processing and identifying audio data, requiring significant computational resources and time due to the complexity of audio data analysis.
Innovation Solution
The system generates audio fingerprints by segmenting audio data, creating spectral representations, and using sparse binary vectors to efficiently match candidate audio data against reference data, reducing computational load through lightweight representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audio identification systems process audio data using full spectral analysis, then identification accuracy is maintained, but computational resources and processing time increase significantly
Solution Approach 1:
The patent extracts only the most salient spectral features (peak frequencies and their magnitudes) from the full audio spectrum, discarding redundant information. This extraction approach maintains identification accuracy by preserving key acoustic characteristics while dramatically reducing computational load and processing time.
Solution Approach 2:
The patent uses lightweight, temporary data structures (simple peak detection results and binary feature vectors) instead of maintaining complex, long-lived spectral representations. These minimal representations are computed on-demand and discarded after matching, reducing memory overhead and enabling faster processing.
2Reliability
If comprehensive audio data analysis is performed to ensure accurate identification, then matching reliability improves, but computational complexity increases
Solution Approach 1:
The patent replaces complex mechanical-style full spectral analysis with a simplified detection mechanism that identifies only peak frequencies and magnitudes. This substitution maintains matching reliability by focusing on the most discriminative features while reducing computational complexity through algorithmic simplification.
Solution Approach 2:
The patent changes the parameter representation from continuous full-spectrum values to discrete peak frequency indices and magnitude ratios. This parameter transformation simplifies the data structure and computational operations while preserving the essential information needed for reliable audio identification.
3Measurement precision
If detailed spectral representations are used for audio matching, then identification precision is maintained, but data processing time increases
Solution Approach 1:
The patent segments the audio frequency spectrum into discrete frequency bins and identifies only the peak frequencies within these bins. This segmentation approach maintains matching precision by preserving peak frequency information while reducing processing time through focused analysis of only the most relevant spectral regions.
Solution Approach 2:
The patent performs partial spectral analysis by detecting only peak frequencies and magnitudes rather than analyzing the entire spectrum in detail. This partial action approach maintains sufficient matching precision for identification while significantly reducing the time required for data processing.
Data Source
AI summary
A machine may be configured to generate one or more audio fingerprints of one or more segments of audio data. The machine may access audio data to be fingerprinted and divide the audio data into segments. For any given segment, the machine may generate a spectral representation from the segment; generate a vector from the spectral representation; generate an ordered set of permutations of the vector; generate an ordered set of numbers from the permutations of the vector; and generate a fingerprint of the segment of the audio data, which may be considered a sub-fingerprint of the audio data. In addition, the machine or a separate device may be configured to determine a likelihood that candidate audio data matches reference audio data.