Google llc (20240304186). AUDIO SIGNAL SYNTHESIS FROM A NETWORK OF DEVICES simplified abstract
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 20240304186 titled 'AUDIO SIGNAL SYNTHESIS FROM A NETWORK OF DEVICES
The abstract describes a patent application for merging first and second audio data to generate merged audio data. The first audio data captures a user's spoken utterance collected by a computing device in an environment, while the second audio data captures the same utterance collected by a different computing device within the same environment. The merging process involves using weight values based on predicted signal-to-noise ratios (SNRs) for each audio data set, determined by processing the audio data through a neural network model.
- Merging first and second audio data to create merged audio data.
- First audio data captures a user's spoken utterance in an environment.
- Second audio data captures the same utterance collected by a different computing device in the same environment.
- Weight values based on predicted signal-to-noise ratios (SNRs) are used for merging.
- SNRs are predicted by processing audio data through a neural network model.
Potential Applications: - Enhancing speech recognition systems. - Improving audio quality in multi-device environments. - Enhancing user experience in voice-controlled devices.
Problems Solved: - Improving audio merging accuracy. - Enhancing speech recognition performance. - Addressing challenges in multi-device audio processing.
Benefits: - Enhanced audio quality. - Improved speech recognition accuracy. - Seamless integration of audio data from multiple sources.
Commercial Applications: Title: Enhanced Audio Merging Technology for Speech Recognition Systems This technology can be utilized in: - Smart home devices. - Virtual assistants. - Conference call systems.
Questions about the Technology: 1. How does this technology improve speech recognition accuracy? 2. What are the potential challenges in merging audio data from different devices?
Frequently Updated Research: Stay updated on advancements in neural network models for audio processing and speech recognition technologies.
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.
