Intel Corporation (20240274148). SOUND SOURCE SEPARATION USING ANGULAR LOCATION simplified abstract
Contents
- 1 SOUND SOURCE SEPARATION USING ANGULAR LOCATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SOUND SOURCE SEPARATION USING ANGULAR LOCATION - 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 Questions about Audio Source Separation
- 1.11 Original Abstract Submitted
SOUND SOURCE SEPARATION USING ANGULAR LOCATION
Organization Name
Inventor(s)
Jesus Ferrer Romero of Guadalajara (MX)
Hector Cordourier Maruri of Guadalajara (MX)
Willem Beltman of West Linn OR (US)
SOUND SOURCE SEPARATION USING ANGULAR LOCATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240274148 titled 'SOUND SOURCE SEPARATION USING ANGULAR LOCATION
Simplified Explanation
The patent application describes systems and methods for audio source separation using deep learning. It involves separating an audio signal from a selected location from other sounds by leveraging spectral and spatial characteristics.
- Uses deep learning to separate audio signals based on azimuth angle location.
- Techniques for steering a virtual microphone towards a selected speaker.
- Neural network learns to separate out various speakers by focusing on spectral and spatial characteristics.
- Allows users to choose which source to listen to.
- Utilizes time-domain and frequency-domain signals to generate separated audio output.
Key Features and Innovation
- Deep learning-based system for audio source separation.
- Utilizes spectral and spatial characteristics to separate out various speakers.
- Allows users to select the source they want to listen to.
- Neural network can focus on multiple sources in different directions and cancel out other sounds.
- Incorporates time-domain and frequency-domain signals for audio separation.
Potential Applications
This technology can be used in:
- Conference calls to isolate and enhance the voice of a specific speaker.
- Audio recording to clean up background noise and focus on the main source.
- Hearing aids to improve speech recognition in noisy environments.
- Virtual assistants to better understand and respond to user commands.
- Music production to isolate and enhance individual instruments in a mix.
Problems Solved
- Eliminates background noise and isolates specific audio sources.
- Improves audio quality and clarity in various applications.
- Enhances user experience by allowing them to focus on desired audio sources.
- Enables better communication and speech recognition in noisy environments.
- Provides a more immersive and personalized audio experience.
Benefits
- Improved audio quality and clarity.
- Enhanced user experience and customization.
- Better communication and speech recognition.
- Reduced background noise for clearer audio.
- Increased immersion and personalization in audio applications.
Commercial Applications
- Title: Advanced Audio Source Separation Technology for Enhanced Communication and Entertainment
- This technology can be applied in industries such as telecommunications, entertainment, healthcare, and consumer electronics.
- Market implications include improved audio products, enhanced user experiences, and increased demand for audio processing technologies.
Questions about Audio Source Separation
How does deep learning improve audio source separation?
Deep learning algorithms can analyze spectral and spatial characteristics of audio signals to effectively separate out different sources, leading to clearer and more accurate audio output.
What are the potential applications of audio source separation technology?
This technology can be used in various industries such as telecommunications, entertainment, healthcare, and consumer electronics to improve audio quality, enhance user experiences, and enable better communication in noisy environments.
Original Abstract Submitted
systems and methods for audio source separation. a deep learning-based system uses an azimuth angle location to separate an audio signal originating from a selected location from other sound. techniques are disclosed for steering a virtual direction of a microphone towards a selected speaker. a deep-learning based audio regression method, which can be implemented as a neural network, learns to separate out various speakers by leveraging spectral and spatial characteristics of all sources. the neural network can focus on multiple sources in multiple respective target directions, and cancel out other sounds. a user can choose which source to listen to. the network can use the time-domain signal and a frequency-domain signal to separate out the target signal and generate a separated audio output. the direction of the selected speaker relative to the microphone array can be input to the system as a vector.
