Microsoft technology licensing, llc (20240127848). QUALITY ESTIMATION MODEL FOR PACKET LOSS CONCEALMENT simplified abstract
Contents
- 1 QUALITY ESTIMATION MODEL FOR PACKET LOSS CONCEALMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 QUALITY ESTIMATION MODEL FOR PACKET LOSS CONCEALMENT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
QUALITY ESTIMATION MODEL FOR PACKET LOSS CONCEALMENT
Organization Name
microsoft technology licensing, llc
Inventor(s)
Carl Lorenz Diener of Tallinn (ES)
QUALITY ESTIMATION MODEL FOR PACKET LOSS CONCEALMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127848 titled 'QUALITY ESTIMATION MODEL FOR PACKET LOSS CONCEALMENT
Simplified Explanation
The abstract of the patent application describes a method for training and employing a quality estimation model for audio signals, involving providing degraded audio signals to packet loss concealment models, obtaining enhanced audio signals, obtaining quality labels for the enhanced audio signals, and training a quality estimation model based on the enhanced audio signals and quality labels.
- Providing degraded audio signals to packet loss concealment models
- Obtaining enhanced audio signals output by the packet loss concealment models
- Obtaining quality labels for the enhanced audio signals
- Training a quality estimation model to estimate audio signal quality based on the enhanced audio signals and quality labels
Potential Applications
The technology described in this patent application could be applied in various industries such as telecommunications, audio processing, and quality assessment systems.
Problems Solved
This technology helps in improving the quality estimation of audio signals that have been degraded due to packet loss, which can be crucial in ensuring better user experience in communication systems.
Benefits
The benefits of this technology include more accurate quality estimation of audio signals, improved performance of packet loss concealment models, and enhanced overall user experience in audio communication systems.
Potential Commercial Applications
The technology could be utilized in telecommunications companies, audio streaming services, quality assessment tools, and any other industry where accurate estimation of audio signal quality is essential.
Possible Prior Art
One possible prior art for this technology could be existing methods for training quality estimation models for audio signals, as well as techniques for packet loss concealment in audio communication systems.
Unanswered Questions
How does this technology compare to existing methods for audio signal quality estimation?
This article does not provide a direct comparison to existing methods for audio signal quality estimation. It would be beneficial to understand the specific advantages and limitations of this technology in comparison to other approaches.
What are the potential limitations or challenges in implementing this technology in real-world applications?
The article does not address the potential limitations or challenges that may arise in implementing this technology in practical settings. It would be important to consider factors such as computational resources, scalability, and compatibility with existing systems.
Original Abstract Submitted
this document relates to training and employing a quality estimation model. one example includes a method or technique that can be performed on a computing device. the method or technique can include providing degraded audio signals to one or more packet loss concealment models, and obtaining enhanced audio signals output by the one or more packet loss concealment models. the method or technique can also include obtaining quality labels for the enhanced audio signals and training a quality estimation model to estimate audio signal quality based at least on the enhanced audio signals and the quality labels.
