PINDROP SECURITY, INC. (20240311474). PRESENTATION ATTACKS IN REVERBERANT CONDITIONS simplified abstract
Contents
PRESENTATION ATTACKS IN REVERBERANT CONDITIONS
Organization Name
Inventor(s)
Nikolay Gaubitch of Atlanta GA (US)
David Looney of Atlanta GA (US)
PRESENTATION ATTACKS IN REVERBERANT CONDITIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240311474 titled 'PRESENTATION ATTACKS IN REVERBERANT CONDITIONS
The abstract describes a patent application for a computing device that utilizes machine-learning architectures to detect presentation attacks in audio signals affected by reverberation degradation.
- The computing device obtains training audio signals with corresponding training impulse responses for reverberation degradation.
- It trains a machine-learning model of a presentation attack detection engine using the training impulse responses to generate acoustic parameters.
- The device then obtains an audio signal with an acoustic impulse response affected by reverberation degradation from one or more rooms.
- It generates acoustic parameters for the audio signal using the machine-learning model.
- Finally, the device calculates an attack score for the audio signal based on the parameters generated by the model.
Potential Applications: - Enhancing security systems by detecting presentation attacks in audio signals. - Improving the accuracy of voice recognition systems in reverberant environments.
Problems Solved: - Addressing the challenge of detecting presentation attacks in audio signals with reverberation degradation. - Enhancing the reliability of audio-based security systems.
Benefits: - Increased accuracy in detecting presentation attacks. - Improved performance of voice recognition systems in challenging acoustic environments.
Commercial Applications: Title: "Reverberation-Degradation Detection System for Audio Security" This technology can be utilized in security systems for banks, government facilities, and other high-security environments to prevent unauthorized access through presentation attacks.
Questions about the technology: 1. How does this technology improve the security of audio-based systems in reverberant environments? 2. What are the potential limitations of using machine-learning models for presentation attack detection in audio signals?
Original Abstract Submitted
embodiments include a computing device that executes software routines and/or one or more machine-learning architectures including obtaining training audio signals having corresponding training impulse responses associated with reverberation degradation, training a machine-learning model of a presentation attack detection engine to generate one or more acoustic parameters by executing the presentation attack detection engine using the training impulse responses of the training audio signals and a loss function, obtaining an audio signal having an acoustic impulse response associated with reverberation degradation caused by one or more rooms, generating the one or more acoustic parameters for the audio signal by executing the machine-learning model using the audio signal as input, and generating an attack score for the audio signal based upon the one or more parameters generated by the machine-learning model.
