BRIA ARTIFICIAL INTELLIGENCE LTD. (20240274126). ATTRIBUTING GENERATED AUDIO CONTENTS TO TRAINING EXAMPLES simplified abstract
Contents
- 1 ATTRIBUTING GENERATED AUDIO CONTENTS TO TRAINING EXAMPLES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ATTRIBUTING GENERATED AUDIO CONTENTS TO TRAINING EXAMPLES - 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 Attribution of Generated Audio Content to Training Examples
- 1.13 Original Abstract Submitted
ATTRIBUTING GENERATED AUDIO CONTENTS TO TRAINING EXAMPLES
Organization Name
BRIA ARTIFICIAL INTELLIGENCE LTD.
Inventor(s)
Yair Adato of Kfar Ben Nun (IL)
Michael Feinstein of Tel Aviv (IL)
Ron Mokady of Ramat Hasaron (IL)
Eyal Gutflaish of Beer Sheva (IL)
Vered Horesh-yaniv of Tel Aviv (IL)
ATTRIBUTING GENERATED AUDIO CONTENTS TO TRAINING EXAMPLES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240274126 titled 'ATTRIBUTING GENERATED AUDIO CONTENTS TO TRAINING EXAMPLES
Simplified Explanation
The patent application describes systems and methods for attributing generated audio content to training examples by analyzing properties of the audio content and training examples.
- Audio content generated using a generative model is received.
- Properties of the generated audio content are determined.
- The audio content associated with training examples is analyzed to determine properties.
- The generated audio content is attributed to a subgroup of training examples based on properties.
- Data records associated with the subgroup are updated.
Key Features and Innovation
- Attribution of generated audio content to training examples.
- Analysis of properties of audio content and training examples.
- Updating data records based on attribution.
Potential Applications
This technology can be used in:
- Music production to attribute generated music to training examples.
- Speech synthesis to improve the quality of generated speech.
- Audio recognition systems to enhance accuracy in identifying audio content.
Problems Solved
- Efficiently attributing generated audio content to training examples.
- Improving the quality and accuracy of generated audio content.
- Enhancing the performance of machine learning models in audio generation tasks.
Benefits
- Improved accuracy in attributing generated audio content.
- Enhanced quality of generated audio content.
- Streamlined processes in analyzing and attributing audio content.
Commercial Applications
Attribution of Generated Audio Content to Training Examples: Enhancing Audio Production and Recognition Systems This technology can be applied in:
- Music production software for accurate attribution of generated music.
- Speech synthesis systems for improved speech generation.
- Audio recognition technologies for more precise identification of audio content.
Prior Art
Research in machine learning models for audio generation and attribution can provide insights into prior art related to this technology.
Frequently Updated Research
Stay updated on advancements in machine learning models for audio generation and attribution to enhance the application of this technology.
Questions about Attribution of Generated Audio Content to Training Examples
How does this technology improve the accuracy of attributing generated audio content?
This technology uses properties of audio content and training examples to accurately attribute generated audio content to specific training examples, enhancing accuracy in the process.
What are the potential applications of this technology beyond audio production?
This technology can also be applied in speech synthesis, audio recognition systems, and other fields requiring accurate attribution of generated audio content.
Original Abstract Submitted
systems, methods and non-transitory computer readable media for attributing generated audio contents to training examples are provided. a first audio content generated using a generative model may be received. the generative model may be a result of training a machine learning model using training examples. each training example may be associated with a respective audio content. properties of the first audio content may be determined. for each training example of the training examples, the respective audio content may be analyzed to determine properties of the respective audio content. the properties of the first audio content and the properties of the audio contents associated with the training examples may be used to attribute the first audio content to a subgroup of the training examples. a respective data-record associated with a source associated with the training examples of the subgroup may be updated based on the attribution.
