MIIR AUDIO TECHNOLOGIES, INC. (20240296182). SYSTEMS AND METHODS FOR FILTERING LARGE AUDIO LIBRARIES USING PERCEPTIVE DISTRIBUTION BINNING simplified abstract
Contents
SYSTEMS AND METHODS FOR FILTERING LARGE AUDIO LIBRARIES USING PERCEPTIVE DISTRIBUTION BINNING
Organization Name
Inventor(s)
Roger Dumas of Wayzata MN (US)
Jon Beck of Minneapolis MN (US)
Aaron Prust of Crystal MN (US)
Paul J. Moe of Minnetonka MN (US)
Daniel J. Levitin of Los Angeles CA (US)
SYSTEMS AND METHODS FOR FILTERING LARGE AUDIO LIBRARIES USING PERCEPTIVE DISTRIBUTION BINNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296182 titled 'SYSTEMS AND METHODS FOR FILTERING LARGE AUDIO LIBRARIES USING PERCEPTIVE DISTRIBUTION BINNING
Abstract: The patent application describes a system and methods for filtering and sorting libraries of audio data by quantitatively processing the audio data to generate metrics for use in filtering and sorting based on distribution bins derived using human listening perception. This includes calculating composite metrics based on values of objective audio metrics for individual audio files in a large library, with each objective audio metric generating a distribution of values. The method involves assigning bins for each value of the objective audio metrics to represent perceptually distinct groups, and combining the bins of each objective audio metric to generate corresponding distribution bins of the values of the composite metric for the large audio library. The large audio library is then filtered using corresponding distribution bins for a plurality of distinct composite metrics.
- Key Features and Innovation:
- Quantitatively processing audio data to generate metrics for filtering and sorting. - Calculating composite metrics based on values of objective audio metrics. - Assigning bins to represent perceptually distinct groups. - Filtering large audio libraries using distribution bins for composite metrics.
- Potential Applications:
- Music streaming platforms for better organization and recommendation. - Audio editing software for efficient sorting and filtering of audio files. - Sound analysis tools for researchers and professionals in the audio industry.
- Problems Solved:
- Inefficient manual sorting and filtering of large audio libraries. - Lack of objective metrics for organizing audio data effectively. - Difficulty in identifying perceptually distinct groups in audio files.
- Benefits:
- Improved organization and filtering of audio libraries. - Enhanced user experience in accessing and managing audio data. - Objective metrics for better understanding and analyzing audio content.
- Commercial Applications:
- "Advanced Audio Library Filtering and Sorting System for Music Streaming Platforms and Audio Editing Software"
- Prior Art:
- Researchers in the field of audio signal processing and music information retrieval may have explored similar methods for analyzing and organizing audio data.
- Frequently Updated Research:
- Stay updated on advancements in audio processing algorithms and machine learning techniques for audio analysis and organization.
Questions about Audio Data Processing: 1. How does the system assign bins to represent perceptually distinct groups in audio files? - The system assigns bins based on the values of objective audio metrics, which are derived using human listening perception to ensure perceptual relevance.
2. What are the potential applications of this technology beyond audio libraries? - This technology can be applied in various fields such as speech recognition, environmental sound analysis, and quality assessment in audio production.
Original Abstract Submitted
system and methods for filtering and sorting libraries of audio data and quantitatively processing audio data to generate metrics for use in filtering and sorting methods based on distribution bins that are, for example, derived using human listening perception. examples include calculating composite metrics based on values of objective audio metrics for individual audio files of a large audio library, each objective audio metrics generating a distribution of values. examples include assigning three or more bins for each value of the objective audio metrics such that the bins represent perceptually distinct groups and, for each composite metric, combining the bins of each objective audio metric of the composite metric to generate corresponding distribution bins of the values of the composite metric for the large audio library. examples include filtering the large audio library using corresponding distribution bins for a plurality of distinct composite metrics.
