Extracting classifying data in music from an audio bitstream
US7295977B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 27, 2001 |
| Grant date | Nov 13, 2007 |
| Priority date | — |
| Expiry date | Jan 17, 2023 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L25/30
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The method of the present invention utilizes machine-learning techniques, particularly Support Vector Machines in combination with a neural network, to process a unique machine-learning enabled representation of the audio bitstream. Using this method, a classifying machine is able to autonomously detect characteristics of a piece of music, such as the artist or genre, and classify it accordingly. The method includes transforming digital time-domain representation of music into a frequency-domain representation, then dividing that frequency data into time slices, and compressing it into frequency bands to form multiple learning representations of each song. The learning representations that result are processed by a group of Support Vector Machines, then by a neural network, both previously trained to distinguish among a given set of characteristics, to determine the classification.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.