Audio Fingerprinting for Carrier Message Detection
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Solution Overview
Problem
Current methods for detecting new or changed carrier audio messages in automated dialing campaigns are inefficient, relying on human listening and prone to errors, which can lead to misclassification of calls and customer complaints.
Innovation Solution
The use of Locality Sensitive Hashing (LSH) to search for similar audio clips within a large dataset, allowing for the unsupervised discovery of new carrier audio messages by encoding audio signals into binary codes and searching for matching hash buckets, thereby reducing the need for human intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human listening is used to detect new or changed carrier audio messages, then the system can identify audio message changes, but the process is inefficient and prone to errors
Solution Approach 1:
The patent replaces the mechanical human listening process with an automated audio fingerprinting system. The system extracts acoustic features from audio messages, generates fingerprints, and compares them using algorithmic matching instead of human perception. This substitution eliminates human error while maintaining high detection accuracy and significantly improves processing efficiency.
Solution Approach 2:
The system enables self-service detection by automatically monitoring its own carrier audio messages without requiring external human intervention. The audio fingerprinting system continuously compares incoming audio messages against known fingerprints, automatically detecting new or changed messages and triggering appropriate actions without human involvement in the detection process.
2Reliability
If human listening is used for detecting carrier audio messages, then detection can be performed, but misclassification of calls occurs due to human error
Solution Approach 1:
The patent replaces error-prone human listening with automated audio fingerprinting technology. The system extracts acoustic features, generates unique fingerprints for each carrier audio message, and performs algorithmic comparison to determine message identity. This eliminates human error in call classification while the modular fingerprinting architecture keeps system complexity manageable.
3Reliability
If automated dialing campaigns are monitored manually, then carrier audio message changes can be detected, but customer complaints increase due to detection errors
Solution Approach 1:
The system performs self-service monitoring by automatically detecting carrier audio message changes through audio fingerprinting. The continuous comparison of audio fingerprints against known messages enables real-time detection without human intervention, eliminating detection errors that lead to customer complaints and enabling immediate response to message changes.
Solution Approach 2:
The audio fingerprinting system operates continuously, constantly comparing incoming audio messages against the database of known fingerprints. This continuous monitoring ensures that carrier audio message changes are detected immediately upon occurrence, preventing misclassification errors and customer complaints by maintaining uninterrupted detection coverage.
Data Source
AI summary
A system and method are presented for unsupervised discovery of similar audio events collected from an automated dialing campaign. Locality Sensitive Hashing (LSH) is used to search for similar audio clips within a large dataset of audio recordings. A database is queried for possible matches between an unknown audio clip and any reference carrier audio message present in the database. The database is updated when new, or changed, carrier audio messages are detected.