18605701. Extracting Ambience From A Stereo Input simplified abstract (Apple Inc.)
Contents
Extracting Ambience From A Stereo Input
Organization Name
Inventor(s)
Ismael H. Nawfal of Redondo Beach CA (US)
Mehrez Souden of Los Angeles CA (US)
Juha O. Merimaa of San Mateo CA (US)
Extracting Ambience From A Stereo Input - A simplified explanation of the abstract
This abstract first appeared for US patent application 18605701 titled 'Extracting Ambience From A Stereo Input
Simplified Explanation: The patent application describes a method for converting first order Ambisonics (FOA) audio into a higher order Ambisonics (HOA) format using machine learning.
- The processor formats each signal of the FOA audio into a stream of audio frames.
- A machine learning model then reformats the FOA audio into the desired HOA format.
- The output audio in the desired HOA format can be rendered according to a chosen playback audio format.
Key Features and Innovation:
- Conversion of FOA audio to HOA format using machine learning.
- Streamlining the process of reformatting audio for different playback formats.
- Enhancing the immersive experience of sound scenes.
Potential Applications:
- Virtual reality and augmented reality applications.
- Gaming industry for realistic audio effects.
- Film and television production for spatial audio.
Problems Solved:
- Simplifying the conversion process between different Ambisonics formats.
- Improving the quality and accuracy of audio rendering in various formats.
Benefits:
- Enhanced audio experience for users.
- Efficient conversion process for audio professionals.
- Versatile application in various industries.
Commercial Applications: The technology can be utilized in virtual reality gaming, audio production studios, and entertainment venues to provide a more immersive audio experience for consumers.
Prior Art: Prior research in the field of Ambisonics audio processing and machine learning algorithms can provide insights into similar technologies and approaches.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for audio processing and spatial audio rendering techniques to enhance the efficiency and accuracy of the conversion process.
Questions about Ambisonics Audio Conversion: 1. How does machine learning improve the conversion process of Ambisonics audio formats? 2. What are the potential challenges in implementing this technology in real-time audio applications?
Original Abstract Submitted
A sound scene is represented as first order Ambisonics (FOA) audio. A processor formats each signal of the FOA audio to a stream of audio frames, provides the formatted FOA audio to a machine learning model that reformats the formatted FOA audio in a target or desired higher order Ambisonics (HOA) format, and obtains output audio of the sound scene in the desired HOA format from the machine learning model. The output audio in the desired HOA format may then be rendered according to a playback audio format of choice. Other aspects are also described and claimed.
