18119137. AUDIO SIGNAL SYNTHESIS FROM A NETWORK OF DEVICES simplified abstract (Google LLC)
AUDIO SIGNAL SYNTHESIS FROM A NETWORK OF DEVICES
Organization Name
Inventor(s)
Dongeek Shin of San Jose CA (US)
AUDIO SIGNAL SYNTHESIS FROM A NETWORK OF DEVICES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18119137 titled 'AUDIO SIGNAL SYNTHESIS FROM A NETWORK OF DEVICES
Simplified Explanation
The patent application describes a method of merging two sets of audio data, each capturing a spoken utterance, collected by separate computing devices within the same environment.
Key Features and Innovation
- Merging first and second audio data to generate merged audio data.
- Using weight values based on predicted signal-to-noise ratios (SNRs) to merge the audio data.
- Predicting SNRs using a neural network model for each set of audio data.
Potential Applications
This technology could be used in voice recognition systems, conference call recording, and audio enhancement applications.
Problems Solved
This technology addresses the challenge of merging audio data captured by different devices to improve overall audio quality and clarity.
Benefits
- Improved audio quality in merged data.
- Enhanced user experience in voice-related applications.
- Better noise reduction and signal clarity.
Commercial Applications
- Voice recognition software development.
- Audio recording and editing tools.
- Communication devices and systems.
Prior Art
Researchers and developers interested in this technology may explore prior art related to audio signal processing, neural network models for audio data analysis, and voice recognition technologies.
Frequently Updated Research
Stay informed about advancements in audio signal processing, neural network algorithms for audio data analysis, and voice recognition system improvements.
Questions about Merging Audio Data
How does merging audio data from different sources benefit users?
Merging audio data improves overall quality and clarity, enhancing user experience in various applications.
What are the key factors to consider when merging audio data captured by different devices?
Key factors include predicted signal-to-noise ratios, weight values for merging, and the use of neural network models for analysis.
Original Abstract Submitted
Merging first and second audio data to generate merged audio data, where the first audio data captures a spoken utterance of a user and is collected by a first computing device within an environment, and the second audio data captures the spoken utterance and is collected by a distinct second computing device that is within the environment. In some implementations, the merging includes merging the first audio data using a first weight value and merging the second audio data using a second weight value. The first and second weight values can be based on predicted signal-to-noise ratios (SNRs) for respective of the first audio data and the second audio data, such as a first SNR predicted by processing the first audio data using a neural network model and a second SNR predicted by processing the second audio data using the neural network model.
