Audio Fingerprint Matching for Real-Time Call Progress Analysis
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Solution Overview
Problem
Current audio identification systems, particularly in call progress analysis, face challenges with high false positive rates, computational intensity, and delayed matching times, making them inefficient for real-time applications and prone to errors in distinguishing live speakers from pre-recorded messages.
Innovation Solution
The implementation of a ternary fingerprint bitmap system that generates hash keys from subdivided acoustic fingerprint bitmaps, allowing for efficient comparison and reducing sensitivity to noise and artifacts, combined with an optimized exhaustive search for real-time matching, enables faster and more accurate identification of audio segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional audio identification systems are used for call progress analysis, then they can identify pre-recorded messages, but they produce high false positive rates and require significant computational resources
Solution Approach 1:
The patent divides the audio signal into multiple frames and extracts acoustic features from each frame independently. By segmenting the audio processing into discrete time frames, the system can identify pre-recorded messages through pattern matching without requiring intensive full-signal analysis, thereby reducing computational complexity while maintaining identification accuracy.
Solution Approach 2:
The patent replaces traditional mechanical audio comparison methods with a digital fingerprinting approach using acoustic feature extraction and pattern matching algorithms. This substitution enables more efficient processing by using computational patterns rather than brute-force audio comparison, reducing the computational burden while improving reliability in distinguishing live speakers from pre-recorded messages.
2Reliability
If traditional audio identification systems are used, then they can detect call progress, but they have delayed matching times and are inefficient for real-time applications
Solution Approach 1:
The patent performs preliminary extraction of acoustic fingerprints and organization of audio data into structured formats before actual identification is needed. By pre-processing and organizing the audio data into searchable patterns, the system enables rapid real-time matching without requiring intensive processing during the actual call progress detection, thus reducing matching time while maintaining detection accuracy.
Solution Approach 2:
The patent replaces traditional time-consuming audio comparison methods with efficient digital pattern matching algorithms. This substitution allows for rapid identification of pre-recorded messages by comparing acoustic fingerprints against a database of known patterns, significantly reducing matching time while maintaining high detection accuracy for real-time applications.
3Productivity
If automated dialing systems use basic call progress analysis, then they can operate efficiently, but they cannot reliably distinguish between live speakers and pre-recorded messages
Solution Approach 1:
The patent replaces basic call progress analysis with advanced acoustic fingerprinting technology that extracts and compares detailed acoustic features. This substitution enables automated dialing systems to reliably distinguish between live speakers and pre-recorded messages by analyzing subtle acoustic patterns, thereby improving distinction accuracy without compromising automated dialing efficiency.
Solution Approach 2:
The patent introduces acoustic fingerprinting as an intermediary layer between the automated dialing system and the audio analysis function. This intermediary extracts distinctive acoustic features and patterns that serve as reliable indicators for distinguishing live speakers from pre-recorded messages, enabling the automated system to make accurate distinctions while maintaining operational efficiency.
Data Source
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.