Audio Fingerprint Bitmaps for Low-False-Positive Network Message Matching
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Solution Overview
Problem
Current audio identification methods, particularly in call progress analysis, face challenges with high false positive rates and computational intensity, making them inefficient for real-time identification of network messages during outbound calls.
Innovation Solution
The implementation of a ternary fingerprint bitmap system that compares candidate audio segments against a database of known recordings, using a combination of binary fingerprint bitmaps and 'Don't Care' bits to mask noise-sensitive regions, allowing for faster and more accurate matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Answering Machine Detection algorithms are used for audio identification, then the system can detect speech patterns, but the false positive rate increases and accuracy decreases
Solution Approach 1:
The audio signal is divided into multiple frames, and each frame is transformed into a spectrogram with specific frequency bins. The fingerprinting process segments the audio into discrete temporal and spectral components, allowing precise localization and comparison of audio features across different recordings.
Solution Approach 2:
The patent transforms audio energy distribution into a visual spectrogram representation where different frequency bins are represented as distinct channels. The binary fingerprint bitmap acts as a simplified 'color map' that highlights specific spectral-temporal patterns, enabling visual and computational comparison of audio characteristics.
2Measurement precision
If detailed audio analysis is performed to improve identification accuracy, then measurement precision increases, but computational intensity and processing time increase
Solution Approach 1:
The patent extracts only the most discriminative features from the full audio signal by creating a binary fingerprint bitmap that captures essential spectral-temporal patterns. Instead of analyzing the entire audio signal in detail, the system extracts key fingerprint characteristics that suffice for accurate identification, significantly reducing computational requirements.
Solution Approach 2:
The patent transforms the continuous audio signal into discrete binary values (0 or 1) for each fingerprint bit. This parameter transformation from continuous energy measurements to discrete binary states simplifies subsequent comparison operations while maintaining identification accuracy, reducing computational complexity for matching operations.
3Productivity
If real-time audio identification is implemented during outbound calls, then call progress analysis efficiency improves, but processing speed requirements increase
Solution Approach 1:
The patent pre-computes and stores fingerprint bitmaps for known audio patterns (such as network messages, voicemail greetings, and other call progress indicators) in a database. During real-time call analysis, the system only needs to compare incoming audio fingerprints against these pre-computed references, enabling rapid identification without performing full audio analysis during the call.
Solution Approach 2:
The patent creates simplified binary fingerprint copies of the original audio signals that retain essential identification characteristics. These fingerprint copies serve as lightweight surrogates for the full audio data, allowing rapid comparison and matching operations during real-time call progress analysis without requiring processing of the complete audio streams.
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.