View source for 17689744. PERCEPTUAL BASS EXTENSION WITH LOUDNESS MANAGEMENT AND ARTIFICIAL INTELLIGENCE (AI) simplified abstract (Samsung Electronics Co., Ltd.)

=PERCEPTUAL BASS EXTENSION WITH LOUDNESS MANAGEMENT AND ARTIFICIAL INTELLIGENCE (AI)=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:Sunil Bharitkar of Stevenson Ranch CA (US)|Sunil Bharitkar of Stevenson Ranch CA (US)]][[Category:Sunil Bharitkar of Stevenson Ranch CA (US)]]

[[:Category:William I. Saba of Santa Clarita CA (US)|William I. Saba of Santa Clarita CA (US)]][[Category:William I. Saba of Santa Clarita CA (US)]]

==PERCEPTUAL BASS EXTENSION WITH LOUDNESS MANAGEMENT AND ARTIFICIAL INTELLIGENCE (AI) - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17689744 titled 'PERCEPTUAL BASS EXTENSION WITH LOUDNESS MANAGEMENT AND ARTIFICIAL INTELLIGENCE (AI)

==Simplified Explanation==

The abstract describes a computer-implemented method for implementing a customizable compressor for sidechain processing associated with a loudspeaker. Machine learning is used to automatically adjust the parameters of the sidechain processing. The method also involves extracting channels, including a low-frequency effects (LFE) channel, for nonlinear signal synthesis. The LFE sidechain channel is mixed into a non-LFE sidechain using a proportional power-sum-based approach, while ensuring the LFE sidechain channel remains within a specified threshold of being level.

* Computer-implemented method for customizable compressor for sidechain processing
* Machine learning used to automatically tune sidechain processing parameters
* Extraction of channels, including low-frequency effects (LFE) channel, for nonlinear signal synthesis
* Proportional power-sum-based mix-in of LFE sidechain channel into non-LFE sidechain
* Maintaining LFE sidechain channel within specified level threshold

==Potential Applications==
* Audio processing and compression in loudspeaker systems
* Sound engineering and production in music and film industries
* Home theater systems and audio equipment

==Problems Solved==
* Manual tuning of sidechain processing parameters can be time-consuming and subjective
* Nonlinear signal synthesis can introduce distortion and imbalance without proper control
* Maintaining the level of the LFE sidechain channel within a specified threshold can be challenging

==Benefits==
* Customizable compressor allows for tailored sidechain processing in loudspeaker systems
* Machine learning automates parameter tuning, saving time and improving accuracy
* Proportional power-sum-based mix-in ensures balanced and controlled nonlinear signal synthesis
* Maintaining LFE sidechain channel within specified level threshold improves audio quality and avoids distortion.


==Original Abstract Submitted==

One embodiment provides a computer-implemented method that includes implementing a customizable compressor for at least one sidechain processing associated with a loudspeaker. Machine learning is applied to automatically tune one or more parameters of the at least one sidechain processing. One or more channels are extracted, including a low-frequency effects (LFE) channel, for nonlinear signal synthesis. A proportional power-sum-based mix-in of an LFE sidechain channel is applied into a non-LFE sidechain. The LFE sidechain channel is maintained within a specified threshold of being level, before and after nonlinear signal synthesis.



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