Apple inc. (20240314509). Extracting Ambience From A Stereo Input simplified abstract

Extracting Ambience From A Stereo Input

Organization Name

apple inc.

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 20240314509 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 a machine learning model.

• **Key Features and Innovation:**

   * Processor formats FOA audio signals into audio frames.
   * Machine learning model reformat the audio into desired HOA format.
   * Output audio in HOA format can be rendered in a playback format of choice.

• **Potential Applications:**

   * Virtual reality audio experiences.
   * Immersive audio in gaming.
   * Audio production and post-production.

• **Problems Solved:**

   * Simplifies the conversion process from FOA to HOA.
   * Enhances the quality and realism of sound scenes.

• **Benefits:**

   * Improved audio quality.
   * Enhanced immersive experiences.
   * Streamlined audio production workflows.

• **Commercial Applications:**

   * Virtual reality content creation.
   * Gaming industry for realistic audio effects.
   * Audio software development for professionals.

• **Prior Art:**

   Prior art related to this technology can be found in research papers on ambisonics audio processing and machine learning in audio applications.

• **Frequently Updated Research:**

   Stay updated on advancements in machine learning models for audio processing and ambisonics technology.

Questions about Ambisonics Audio Processing: 1. How does the use of machine learning improve the conversion of FOA to HOA audio formats? 2. What are the potential limitations of using machine learning in audio processing tasks?

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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.