Harman International Industries, Incorporated (20240357279). METHOD FOR DETERMINING A FREQUENCY RESPONSE OF AN AUDIO SYSTEM simplified abstract
Contents
- 1 METHOD FOR DETERMINING A FREQUENCY RESPONSE OF AN AUDIO SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR DETERMINING A FREQUENCY RESPONSE OF AN AUDIO SYSTEM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Audio System Frequency Response
- 1.13 Original Abstract Submitted
METHOD FOR DETERMINING A FREQUENCY RESPONSE OF AN AUDIO SYSTEM
Organization Name
Harman International Industries, Incorporated
Inventor(s)
Andrey Viktorovich Filimonov of Kamenki (RU)
Mikhail Sergeevich Kleshnin of Nizhny Novgorod region (RU)
Anna Yurievna Kerbeneva of Nizhny Novgorod (RU)
Sean Edward Olive of Oak Park CA (US)
METHOD FOR DETERMINING A FREQUENCY RESPONSE OF AN AUDIO SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240357279 titled 'METHOD FOR DETERMINING A FREQUENCY RESPONSE OF AN AUDIO SYSTEM
Simplified Explanation
The patent application describes a method for determining the frequency response of an audio system using a generative adversarial network (GAN) trained on reference audio systems.
Key Features and Innovation
- Utilizes a GAN to predict frequency responses of audio systems based on evaluator scorings.
- Trains the GAN discriminator on measured frequency responses of reference audio systems.
- Trains the GAN generator on evaluator scorings to predict frequency responses.
- Processes a production dataset to predict the frequency response of a production audio system.
Potential Applications
This technology can be used in audio engineering to optimize the performance of audio systems. It can also be applied in the development of audio equipment and software for sound quality enhancement.
Problems Solved
Provides a more efficient and accurate method for determining the frequency response of audio systems. Helps in evaluating and improving the performance of audio systems based on predicted frequency responses.
Benefits
Enhances the quality of audio systems by predicting accurate frequency responses. Saves time and resources compared to traditional methods of measuring frequency responses.
Commercial Applications
- Audio equipment manufacturing companies can use this technology to develop high-quality audio systems.
- Audio software developers can integrate this method to enhance the sound quality of their products.
Prior Art
There may be prior research on using GANs for audio signal processing and analysis that could be relevant to this technology.
Frequently Updated Research
Stay updated on advancements in GAN technology for audio signal processing to improve the accuracy and efficiency of frequency response prediction.
Questions about Audio System Frequency Response
How does the GAN technology improve the prediction of frequency responses in audio systems?
The GAN technology uses a discriminator and generator to process evaluator scorings and predict frequency responses, resulting in more accurate predictions.
What are the potential limitations of using GANs for audio system frequency response prediction?
One potential limitation could be the need for a large amount of training data to ensure accurate predictions.
Original Abstract Submitted
a computer-implemented method for determining a frequency response of an audio system, the method comprising: training a generative adversarial network, gan, discriminator on a first training dataset comprising measured frequency responses of reference audio systems to a test signal and an evaluator scoring of the audio system to predict a predicted scoring for the reference audio systems, training a gan generator on a second training dataset comprising evaluator scorings to predict a predicted frequency response for the reference audio systems, wherein training the gan generator comprises processing the predicted frequency response by the trained gan discriminator to predict a predicted scoring; and processing a production dataset comprising an input scoring of a production audio system by the trained gan generator to predict a frequency response of the production audio system.
