Ai-based detection of anomalies in audiovisual data
An AI-based glitch detection system using neural networks efficiently identifies glitches in audiovisual data, improving reliability and reducing costs by automating the detection process.
WO2025208020A1 Publication Date: 2025-10-02ADVANCED MICRO DEVICES INC
5 Cites 0 Cited by
Patent Information
- Application Number
- PCT/US2025/022002
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2024-03-28
- Filing Date
- 2025-03-28
- Publication Date
- 2025-10-02
AI Technical Summary
Technical Problem
Existing methods for detecting glitches in audiovisual data are laborious and unreliable, particularly for intermittently occurring defects, necessitating a more efficient and automated approach.
Method used
An AI-based glitch detection system utilizing a feature extraction component, glitch detection model, and logging component to analyze audiovisual data for anomalies, employing neural networks to classify images and audio segments for glitches, and generate records for validation.
Benefits of technology
The system enhances the reliability and efficiency of glitch detection, reducing human effort and costs by accurately identifying glitches and their sources, facilitating faster and more cost-effective system validation.
✦ Generated by Eureka AI based on patent content.
Abstract
Using artificial intelligence (AI)-based techniques to detect glitches in audiovisual data can improve the reliability of glitch detection and reduce the cost of system validation and / or maintenance. An AI-based method for detecting glitches in an image can include extracting one or more features from the image; providing the image and the extracted features as inputs to a model; and generating, by the model, a classification output indicating whether the image is glitched. An AI-based method for detecting glitches in an audio data segment can include generating an image including a spectrogram of the audio data segment; providing, the image as input to a model; and generating, by the model, a classification output indicating whether the audio data segment represented by the image is glitched. Records of the glitches can be generated, and the validation status of a system-under-test (SUT) can be determined based on the records.
Need to check novelty before this filing date? Find Prior Art