Nokia technologies oy (20240298133). Apparatus, Methods and Computer Programs for Training Machine Learning Models simplified abstract
Contents
- 1 Apparatus, Methods and Computer Programs for Training Machine Learning Models
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Apparatus, Methods and Computer Programs for Training Machine Learning Models - 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 Spatial Sound Estimation
- 1.13 Original Abstract Submitted
Apparatus, Methods and Computer Programs for Training Machine Learning Models
Organization Name
Inventor(s)
Juha Tapio Vilkamo of Helsinki (FI)
Mikko Johannes Honkala of Espoo (FI)
Apparatus, Methods and Computer Programs for Training Machine Learning Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240298133 titled 'Apparatus, Methods and Computer Programs for Training Machine Learning Models
Simplified Explanation
The patent application describes an apparatus that uses machine learning to estimate spatial metadata for spatial sound distributions. It involves capturing data related to sound distributions and training a model to estimate spatial properties based on the captured data.
Key Features and Innovation
- Circuitry for training a machine learning model to estimate spatial metadata for spatial sound distributions.
- Obtaining first capture data related to spatial sound distributions from a target device with multiple microphone signals.
- Obtaining second capture data using a reference capture method to train the machine learning model.
- Training the model to estimate spatial properties of sound distributions based on the captured data.
Potential Applications
This technology can be used in audio recording devices, virtual reality systems, and sound processing applications where accurate spatial sound estimation is crucial.
Problems Solved
This technology addresses the challenge of accurately estimating spatial properties of sound distributions, which is essential for creating immersive audio experiences.
Benefits
- Improved spatial sound estimation accuracy.
- Enhanced audio recording and playback quality.
- Better spatial awareness in virtual reality environments.
Commercial Applications
- Audio recording equipment manufacturers can integrate this technology to enhance the spatial sound capture capabilities of their devices.
- Virtual reality companies can use this technology to create more realistic and immersive audio experiences for users.
Prior Art
Readers can explore prior research on machine learning models for spatial sound estimation and audio signal processing to understand the existing technology landscape.
Frequently Updated Research
Researchers are continually exploring new techniques and algorithms to improve spatial sound estimation using machine learning models.
Questions about Spatial Sound Estimation
How does machine learning improve spatial sound estimation accuracy?
Machine learning algorithms can analyze large amounts of spatial sound data to learn patterns and relationships, enabling more accurate estimation of spatial properties.
What are the potential challenges in implementing machine learning for spatial sound estimation?
Challenges may include data preprocessing, model training, and optimizing the algorithms for real-time applications.
Original Abstract Submitted
an apparatus includes circuitry for training a machine learning model such as a neural network to estimate spatial metadata for a spatial sound distribution. the apparatus includes circuitry for obtaining first capture data for a machine learning model where the first capture data is related to a plurality of spatial sound distributions and where the first capture data relates to a target device configured to obtain at least two microphone signals. the apparatus also includes circuitry for obtaining second capture data for the machine learning model where the second capture data is obtained using the same spatial sound distributions and where the data includes information indicative of spatial properties of the spatial sound distributions and the data is obtained using a reference capture method. the apparatus also includes circuitry for training the machine learning model to estimate the second capture data based on the first capture data.
