Audio Fingerprint Matching for Real-Time Network Message Identification
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Solution Overview
Problem
Current audio identification systems, particularly in call progress analysis, face challenges with high false positive rates and computational intensity, making them inefficient and inaccurate for real-time identification of network messages during outbound calls.
Innovation Solution
The implementation of a ternary fingerprint bitmap system that masks sensitive bits to noise and artifacts, combined with an optimized exhaustive search using hash keys, enables faster and more accurate matching of acoustic fingerprints, reducing latency and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional CPA algorithms with low- and high-pass frequency discriminators are used, then call progress tones can be interpreted, but false positive rates increase and accuracy decreases
Solution Approach 1:
The patent segments the audio signal into multiple overlapping frames and creates separate fingerprint bitmaps for each frame. By comparing multiple frame-level bitmaps rather than processing the entire audio signal as one unit, the system achieves more precise localization of matches and reduces false positives while maintaining high accuracy in network message identification.
2Measurement precision
If exhaustive search of acoustic fingerprints is performed, then accurate identification can be achieved, but computational intensity and processing time increase
Solution Approach 1:
The patent divides the exhaustive search process into frame-level segments, where each audio frame generates a fingerprint bitmap that is independently compared against database entries. This segmentation allows the system to achieve accurate identification through comprehensive comparison while reducing overall computational intensity by processing smaller, manageable units in parallel rather than analyzing the entire audio signal simultaneously.
Solution Approach 2:
The patent implements a two-stage comparison process: first comparing fingerprint bitmaps at the frame level for quick matching, and only performing full audio signal analysis when frame-level matches are found. This partial action approach achieves accurate identification by focusing computational resources on promising candidates rather than performing exhaustive analysis on all possible matches, thereby reducing overall computational intensity.
3Reliability
If frame-level fingerprint bitmap comparison is implemented, then false negatives are reduced, but processing complexity increases
Solution Approach 1:
The patent segments the audio signal into multiple overlapping frames, each generating a fingerprint bitmap. By comparing these frame-level bitmaps against database entries, the system reduces false negatives because even if one frame is misidentified, other frames provide redundant verification. The segmentation approach manages processing complexity by breaking down the complex task of entire-signal analysis into simpler, independent frame-level comparisons that can be processed efficiently.
Data Source
Figure 1A~1C
Figure 2
Figure 3A
AI summary
Systems and methods for the matching of datasets, such as input audio segments, with known datasets in a database are disclosed. In an illustrative embodiment, the use of the presently disclosed systems and methods is described in conjunction with recognizing known network message recordings encountered during an outbound telephone call. The methodologies include creation of a ternary fingerprint bitmap to make the comparison process more efficient. Also disclosed are automated methodologies for creating the database of known datasets from a larger collection of datasets.