Adobe Inc. (20240331720). STUDIO QUALITY AUDIO ENHANCEMENT simplified abstract
Contents
STUDIO QUALITY AUDIO ENHANCEMENT
Organization Name
Inventor(s)
Zeyu Jin of San Francisco CA (US)
Adam Finkelstein of Princeton NJ (US)
STUDIO QUALITY AUDIO ENHANCEMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240331720 titled 'STUDIO QUALITY AUDIO ENHANCEMENT
Simplified Explanation: The patent application describes a method for converting audio data to studio quality audio using machine learning models.
- Machine learning models predict acoustic features and generate studio quality audio.
- A spectral mask is applied to the audio data during the prediction of acoustic features.
Key Features and Innovation:
- Use of machine learning models to predict acoustic features for audio data conversion.
- Application of a spectral mask to enhance the prediction process.
- Generation of studio quality audio from the predicted acoustic features and original audio data.
Potential Applications: This technology can be used in music production, audio editing, and sound engineering to enhance the quality of audio recordings.
Problems Solved: This technology addresses the challenge of converting audio data to studio quality audio with improved accuracy and efficiency.
Benefits:
- Improved audio quality in studio recordings.
- Enhanced sound editing capabilities.
- Streamlined audio production processes.
Commercial Applications: The technology can be applied in music studios, film production companies, and audio editing software to improve the quality of audio recordings and enhance the overall sound production.
Prior Art: Researchers and developers in the field of audio processing and machine learning may have explored similar methods for enhancing audio quality using predictive models.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for audio processing and the integration of spectral masks in audio enhancement techniques.
Questions about Audio Data Conversion to Studio Quality Audio: 1. How does the spectral mask improve the prediction of acoustic features in audio data conversion? 2. What are the potential limitations of using machine learning models for generating studio quality audio?
Original Abstract Submitted
embodiments are disclosed for converting audio data to studio quality audio data. the method includes obtaining an audio data having a first quality for conversion to studio quality audio. a first machine learning model predicts a set of acoustic features. a spectral mask is applied to the audio data during the prediction of the set of acoustic features. a second machine learning model generates studio quality audio from the set of acoustic features and the audio data.
