NVIDIA Corporation (20240311080). DYNAMICALLY PREVENTING AUDIO ARTIFACTS simplified abstract
Contents
DYNAMICALLY PREVENTING AUDIO ARTIFACTS
Organization Name
Inventor(s)
Utkarsh Vaidya of Santa Clara CA (US)
Sumit Bhattacharya of Santa Clara CA (US)
DYNAMICALLY PREVENTING AUDIO ARTIFACTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240311080 titled 'DYNAMICALLY PREVENTING AUDIO ARTIFACTS
The disclosure pertains to a process that can predict and prevent audio artifacts from occurring by monitoring systems, processes, and execution threads on a larger system/device using learning algorithms such as deep neural networks (DNNs).
- The process generates predictions of potential audio artifacts and recommends system adjustments to prevent them.
- Recommendations may include changes to processing system frequency, memory frequency, and audio buffer size.
- System adjustments can be reversed fully or in steps after the audio artifact has been prevented.
Potential Applications: - Audio systems in mobile devices - In-vehicle audio systems - Gaming consoles
Problems Solved: - Preventing audio glitches - Enhancing user experience with audio systems
Benefits: - Improved audio quality - Enhanced system performance - Reduced user frustration with audio artifacts
Commercial Applications: Title: "Advanced Audio Artifact Prevention Technology" This technology can be utilized in smartphones, tablets, laptops, gaming consoles, and automotive audio systems to provide users with a seamless audio experience.
Questions about the technology: 1. How does the learning algorithm predict potential audio artifacts?
- The learning algorithm analyzes data collected from system monitoring to identify patterns and predict when audio artifacts are likely to occur.
2. What are the key components of the system adjustments recommended to prevent audio glitches?
- The system adjustments may involve changes to processing system frequency, memory frequency, and audio buffer size to mitigate the risk of audio artifacts.
Original Abstract Submitted
the disclosure is directed to a process that can predict and prevent an audio artifact from occurring. the process can monitor the systems, processes, and execution threads on a larger system/device, such as a mobile or in-vehicle device. using a learning algorithm, such as deep neural network (dnn), the information collected can generate a prediction of whether an audio artifact is likely to occur. the process can use a second learning algorithm, which also can be a dnn, to generate recommended system adjustments that can attempt to prevent the audio glitch from occurring. the recommendations can be for various systems and components on the device, such as changing the processing system frequency, the memory frequency, and the audio buffer size. after the audio artifact has been prevented, the system adjustments can be reversed fully or in steps to return the system to its state prior to the system adjustments.
