18676243. DYNAMICALLY PREVENTING AUDIO ARTIFACTS simplified abstract (NVIDIA Corporation)
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 18676243 titled 'DYNAMICALLY PREVENTING AUDIO ARTIFACTS
The disclosure pertains to a process that can predict and prevent audio artifacts from occurring in systems, such as mobile or in-vehicle devices. By utilizing a learning algorithm like a deep neural network (DNN), the process can collect information to forecast the likelihood of an audio glitch.
- The process monitors systems, processes, and execution threads on a larger system/device.
- A second learning algorithm, also potentially a DNN, generates recommended system adjustments to prevent the audio artifact.
- Recommendations may include changing processing system frequency, memory frequency, and audio buffer size.
- System adjustments can be reversed fully or gradually after preventing the audio artifact.
Potential Applications: - Automotive industry for in-vehicle entertainment systems - Mobile device manufacturers for audio quality enhancement
Problems Solved: - Predicting and preventing audio artifacts in real-time - Improving user experience by avoiding audio glitches
Benefits: - Enhanced audio quality on devices - Increased user satisfaction with uninterrupted audio playback
Commercial Applications: Title: Predictive Audio Artifact Prevention Technology This technology can be utilized in various consumer electronics to ensure high-quality audio performance, leading to improved customer satisfaction and brand loyalty.
Questions about Predictive Audio Artifact Prevention Technology: 1. How does the learning algorithm help in predicting audio artifacts?
The learning algorithm analyzes system data to identify patterns that indicate the likelihood of an audio glitch, enabling proactive prevention measures.
2. What are the potential long-term benefits of implementing this technology in devices?
Implementing this technology can lead to a significant reduction in audio glitches, resulting in a seamless user experience and increased customer loyalty over time.
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.
