Dynamically preventing audio underrun using machine learning
US10896021B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 26, 2019 |
| Grant date | Jan 19, 2021 |
| Priority date | — |
| Expiry date | Feb 26, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosure is directed to a process that can predict an audio glitch, and then attempt to preempt the audio glitch. The process can monitor the systems, processes, and execution threads on a larger system or device, such as a mobile device or an in-vehicle device. Using a learning algorithm, such as deep neural network (DNN), the information collected can generate a prediction of whether an audio glitch is likely to occur. An audio glitch can be an audio underrun condition. 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 underrun condition has abated, the system adjustments can be reversed fully or in steps to return the system to its state prior to the system adjustments.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.