Parameterized temporal feature analysis

• US 20060196337A1
• Filed: 04/21/2004
• Published: 09/07/2006
• Est. Priority Date: 04/24/2003
• Status: Active Grant

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Breebart, Dirk Jeroen, McKinney, Martin Franciscus
• Granted Patent: US 8,311,821 B2
• Time in Patent Office: Days
• US Class Current: 84/1
• CPC Class Codes: G06F 16/45 Clustering; Classification
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/70 of video data

Abstract

A method (1) for classifying at least one audio signal (A) into at least one audio class (AC), the method (1) comprising the steps of analyzing (10) said audio signal to extract at least one predetermined audio feature, performing (12) a frequency analysis on a set of values of said audio feature at different time instances, deriving (12) at least one further audio feature representing a temporal behavior of said audio feature based on said frequency analysis, and classifying (14) said audio signal based on said further audio feature. With the further audio feature, information is obtained about the temporal fluctuation of an audio feature, which may be advantageous for a classification of audio.

Claims

1. A method (1) for classifying at least one audio signal (A) into at least one audio class (AC), the method (1) comprising the steps of:

analyzing (10) said audio signal to extract at least one predetermined audio feature;

performing (12) a frequency analysis on a set of values of said audio feature at different time instances;

deriving (12) at least one further audio feature representing a temporal behavior of said audio feature based on said frequency analysis; and

classifying (14) said audio signal based on said further audio feature.

2. The method as claimed in claim 1, wherein said at least one predetermined audio feature comprises at least one of the following audio features:

root-mean-square (RMS) level;

spectral centroid (S_f);

bandwidth (B_f);

zero-crossing rate (R_z);

spectral roll-off frequency f_r);

band energy ratio (B_r);

delta spectrum magnitude (f_d);

pitch (7); and

pitch strength (S).

3. The method as claimed in claim 1, wherein said predetermined audio feature comprises at least one mel-frequency cepstral coefficient (MFCC).

4. The method as claimed in claim 1, wherein said predetermined audio feature comprises at least one of the psycho-acoustic (PA) audio features loudness and sharpness.

5. The method as claimed in claim 1, wherein said deriving step comprises the steps of:

calculating an average (DC) value of said set of values of said audio feature at different time instances;

defining at least one frequency band;

calculating the amount of energy within said frequency band from said frequency analysis; and

defining said further audio feature as said amount of energy in dependence on said average (DC) value.

6. The method as claimed in claim 5, wherein at least one of the following modulation frequency bands are used in said parameterizing step:

1-2 Hz;

3-15 Hz; and

20-150 Hz;

7. The method as claimed in claim 1, wherein said at least one further audio feature is defined as at least one coefficient (C(m)) obtained by performing a discrete cosine transformation (DCT) on the result of said frequency analysis.

8. A system (20) for classifying at least one audio signal into at least one audio class, the system comprising:

means (10) for analyzing said audio signal to extract at least one predetermined audio feature;

means (12) for performing a frequency analysis on a set of values of said audio feature at different time instances;

means (12) for deriving at least one further audio feature representing a temporal behavior of said audio feature based on said frequency analysis; and

means (14) for classifying said audio signal based on said further audio feature.

9. A music system (2) comprising:

means (24) for playing audio data from a medium (22); and

a system (20) as claimed in claim 8 for classifying said audio data.

10. A multi-media system (3) comprising:

means (34) for playing audio data from a medium (32);

a system (20) as claimed in claim 8 for classifying said audio data;

means (36) for displaying video data from a further medium (32);

means (38) for analyzing said video data; and

means (34) for combining the results obtained from analyzing (38) said video data with the results obtained from classifying (20) said audio data.

11. A signal (22, 32) comprising at least one further audio feature obtained by:

analyzing (10) an audio signal to extract at least one predetermined audio feature;

performing (12) a frequency analysis on a set of values of said audio feature at different time instances; and

deriving (12) said at least one further audio feature representing a temporal behavior of said audio feature based on said frequency analysis.

12. An audio receiver (2) comprising means to receive an audio signal and means to receive a signal (22, 32) comprising at least one further audio feature obtained by:

analyzing (10) said audio signal to extract at least one predetermined audio feature;

performing (12) a frequency analysis on a set of values of said audio feature at different time instances; and

deriving (12) said at least one further audio feature representing a temporal behavior of said audio feature based on said frequency analysis,

the audio receiver (24) further comprising means (14) for classifying said audio signal based on said further audio feature.