Radio Distress Call Authentication Using Voice and Hoax Classification
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Solution Overview
Problem
Computers struggle to accurately identify authentic distress calls from background noise and differentiate them from hoax calls, leading to potential misclassification and inefficient emergency response.
Innovation Solution
A system utilizing machine learning models to detect and extract human voice from radio signals, classify it as either authentic or hoax based on verbal content, and generate alerts, reducing the subjectivity and improving accuracy in distress call identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators manually identify distress calls from radio noise, then subjective judgment can be applied, but the process is inefficient and prone to human error and fatigue
Solution Approach 1:
The patent replaces the mechanical system of human listening and judgment with an automated computer-based system that uses signal processing and machine learning algorithms to detect and classify distress calls, eliminating human fatigue and subjectivity while maintaining high accuracy
Solution Approach 2:
The system enables self-service by allowing the computer to autonomously perform distress call detection, classification, and authentication without human intervention, with the automated system serving itself to identify and respond to emergency situations
2Productivity
If automated systems detect distress calls in background noise, then response efficiency improves, but false alarms from hoax calls increase
Solution Approach 1:
The patent segments the distress call detection process into distinct stages: initial detection of voice in noise, extraction of the voice signal, recognition of speech content, and classification as authentic or hoax. This segmentation allows each stage to be optimized independently, with the classification stage specifically designed to filter out false alarms
Solution Approach 2:
The system incorporates feedback mechanisms where the classification results from analyzing verbal content and speech patterns are fed back to verify initial detections, allowing the system to distinguish authentic distress calls from hoax calls and reduce false alarms while maintaining rapid response capability
3Measurement precision
If computers analyze verbal content to classify distress calls, then authentication accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces speech recognition technology as an intermediary that converts verbal content into text or structured data, which can then be analyzed by classification algorithms. This intermediary layer simplifies the overall system architecture by breaking down the complex task of voice analysis into manageable stages: voice detection, speech-to-text conversion, and content-based classification
Data Source
AI summary
Systems, methods, and other embodiments associated with computer distress-call detection and authentication are described. In one embodiment, a method includes detecting a human voice in audio content of a radio signal. Speech is recognized in the human voice to transform the human voice into text and vocal metrics. Feature scores are generated that represent features of the recognized speech based at least in part on the vocal metrics. The human voice is then classified as either a hoax distress call or an authentic distress call based on the feature scores. An alert is then presented indicating that the human voice is one of the hoax distress call or the authentic distress call.