Intel corporation (20240223948). MICROPHONE CHANNEL SELF-NOISE SILENCING simplified abstract
Contents
- 1 MICROPHONE CHANNEL SELF-NOISE SILENCING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MICROPHONE CHANNEL SELF-NOISE SILENCING - A simplified explanation of the abstract
- 1.4 Potential Applications
- 1.5 Problems Solved
- 1.6 Benefits
- 1.7 Commercial Applications
- 1.8 Prior Art
- 1.9 Frequently Updated Research
- 1.10 Questions about Self-Noise Reduction Technology
- 1.11 Original Abstract Submitted
MICROPHONE CHANNEL SELF-NOISE SILENCING
Organization Name
Inventor(s)
Adam Kupryjanow of Gdansk (PL)
Przemyslaw Maziewski of Gdansk (PL)
Lukasz Pindor of Pruszcz Gdanski (PL)
Sebastian Rosenkiewicz of Gdansk (PL)
MICROPHONE CHANNEL SELF-NOISE SILENCING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240223948 titled 'MICROPHONE CHANNEL SELF-NOISE SILENCING
The patent application describes a user computing device with a microphone that generates an audio signal and a self-noise silencer that generates a feature set corresponding to the audio signal. This feature set includes magnitude values for different frequency components in the audio signal. A machine learning model is trained to infer frequencies contributing to self-noise generated at the microphone based on this feature set. An attenuation mask is then generated to identify attenuation values for certain frequency components, which is applied to remove self-noise from the audio signal and produce a denoised version.
- User computing device with microphone and self-noise silencer
- Feature set generated for audio signal with magnitude values for frequency components
- Machine learning model trained to infer frequencies contributing to self-noise
- Attenuation mask generated to identify attenuation values for frequency components
- Removal of self-noise from audio signal to produce denoised version
Potential Applications
This technology could be used in various audio recording devices to improve the quality of recorded audio by reducing self-noise generated by the microphone.
Problems Solved
This technology addresses the issue of self-noise in audio signals, which can degrade the quality of recordings and affect the overall user experience.
Benefits
The benefits of this technology include improved audio quality, enhanced user experience, and the ability to produce cleaner recordings with reduced self-noise.
Commercial Applications
Title: Advanced Self-Noise Reduction Technology for Audio Recording Devices This technology could have commercial applications in the audio recording industry, including in professional recording studios, podcasting equipment, and consumer audio devices.
Prior Art
Readers interested in prior art related to this technology could start by exploring patents and research papers on noise reduction techniques in audio processing and machine learning applications in audio signal processing.
Frequently Updated Research
Researchers are continually exploring new methods and algorithms for noise reduction in audio signals, including the use of machine learning models to improve the quality of recorded audio. Stay updated on the latest advancements in this field for potential future applications.
Questions about Self-Noise Reduction Technology
How does this technology compare to traditional noise reduction methods?
This technology differs from traditional noise reduction methods by utilizing machine learning models to identify and attenuate self-noise in audio signals, offering a more advanced and efficient solution.
What are the potential limitations of this technology in real-world applications?
While this technology shows promise in reducing self-noise in audio signals, potential limitations may include computational complexity, calibration requirements, and adaptability to different recording environments.
Original Abstract Submitted
a user computing device includes a microphone to generate an audio signal and a self-noise silencer to generate a feature set corresponding to the audio signal, where the input feature identifies, for each of a plurality of frequency components in the audio signal, a respective magnitude value. at least a portion of the feature set is provided as an input to a machine learning model trained to infer frequencies contributing to self-noise generated at the microphone. an attenuation mask is generated, based on an output of the machine learning model, that identifies an attenuation value for at least a subset of the plurality of frequency components. the attenuation mask is applied to at least the subset of the magnitude values of the plurality of frequency components to remove self-noise from the audio signal and generate a denoised version of the audio signal.
