System and methods for providing automatic classification of media entities according to consonance properties

• US 20050097075A1
• Filed: 11/12/2004
• Published: 05/05/2005
• Est. Priority Date: 07/06/2000
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Weare, Christopher B., Hoekman, Jeffrey S.
• Granted Patent: US 7,756,874 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/48 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/634 Query by example, e.g. query by humming
• CPC Class Codes: G06F 16/635 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/639 using playlists
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content
• CPC Class Codes: G10L 17/26 Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
• CPC Class Codes: G10L 25/48 specially adapted for particular use
• CPC Class Codes: G11B 27/105 of operating discs
• CPC Class Codes: G11B 27/28 by using information signals recorded by the same method as the main recording G11B27/22 takes precedence
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation

Abstract

In connection with a classification system for classifying media entities that merges perceptual classification techniques and digital signal processing classification techniques for improved classification of media entities, a system and methods are provided for automatically classifying and characterizing musical consonance properties of media entities. Such a system and methods may be useful for the indexing of a database or other storage collection of media entities, such as media entities that are audio files, or have portions that are audio files. The methods also help to determine media entities that have similar consonance by utilizing classification chain techniques that test distances between media entities in terms of their properties. For example, a neighborhood of songs may be determined within which each song has a similar consonance.

Claims

1. A method of classifying data according to consonance properties of the data, comprising:

assigning to each media entity of a plurality of media entities in a data set to at least one consonance class;

processing each media entity of said data set to extract at least one consonance characteristic based on digital signal processing of each media entity;

generating a plurality of consonance vectors for said plurality of media entities, wherein each consonance vector includes said at least one consonance class and at least one consonance characteristic based on digital signal processing; and

forming a classification chain based upon said plurality of feature vectors.

2. A method according to claim 1, further comprising:

processing an unclassified media entity to extract at least one consonance characteristic based on digital signal processing of the unclassified media entity;

generating a vector for the unclassified media entity including said at least one digital signal processing consonance characteristic;

presenting the vector for the unclassified media entity to the classification chain; and

classifying the unclassified entry with an estimate of the consonance class by calculating the representative consonance class of the subset of the plurality of vectors of the classification chain located in the neighborhood of the vector for the unclassified entity.

3. A method according to claim 2, further including calculating a neighborhood distance that defines a distance within which two vectors in the classification chain space are in the same neighborhood for purposes of being in the same consonance class.

4. A method according to claim 2, wherein said classifying of the unclassified entry includes classifying the unclassified entry with a median consonance class represented by the neighborhood.

5. A method according to claim 2, wherein said consonance class is described by a numerical value and said classifying of the unclassified entry includes classifying the unclassified entry with a mean of numerical consonance values found in the neighborhood.

6. A method according to claim 2, wherein said classifying includes returning at least one number indicating the level of confidence of the consonance class estimate.

7. A computer readable medium bearing computer executable instructions for:

assigning to each media entity of a plurality of media entities in a data set to at least one. consonance class;

processing each media entity of said data set to extract at least one consonance characteristic based on digital signal processing of each media entity;

generating a plurality of consonance vectors for said plurality of media entities, wherein each consonance vector includes said at least one consonance class and at least one consonance characteristic based on digital signal processing; and

forming a classification chain based upon said plurality of feature vectors.

8. A modulated data signal carrying computer executable instructions for:

assigning to each media entity of a plurality of media entities in a data set to at least one consonance class;

processing each media entity of said data set to extract at least one consonance characteristic based on digital signal processing of each media entity;

generating a plurality of consonance vectors for said plurality of media entities, wherein each consonance vector includes said at least one consonance class and at least one consonance characteristic based on digital signal processing; and

forming a classification chain based upon said plurality of feature vectors.

9. At least one computing device comprising means for:

assigning to each media entity of a plurality of media entities in a data set to at least one consonance class;

processing each media entity of said data set to extract at least one consonance characteristic based on digital signal processing of each media entity;

generating a plurality of consonance vectors for said plurality of media entities, wherein each consonance vector includes said at least one consonance class and at least one consonance characteristic based on digital signal processing; and

forming a classification chain based upon said plurality of feature vectors.

10. A computing system, comprising:

a computing device including;

a classification chain data structure stored thereon having a plurality of classification vectors, wherein each vector includes data representative of a consonance class as classified by humans and consonance characteristics as determined by digital signal processing; and

processing means for comparing an unclassified media entity to the classification chain data structure to determine an estimate of the consonance class of the unclassified media entity.

11. A computing system according to claim 10, wherein said determining of an estimate of the consonance class includes returning at least one number indicating the level of confidence of the consonance class assignment.

12. A method according to claim 11, wherein the performance level of the classification chain improves over time due to the examination of unclassified media entities that have a low confidence level associated with the consonance class assignment.

13. A classification chain data structure utilized in connection with the classification of consonance of new unclassified media entities, comprising:

a plurality of classification vectors, wherein each vector includes;

consonance data as classified by humans; and

consonance data determined by digital signal processing techniques.