Intelligent classification system of sound signals and method thereof

• US 20080082323A1
• Filed: 11/03/2006
• Published: 04/03/2008
• Est. Priority Date: 09/29/2006
• Status: Abandoned Application

• Current Assignee: National Chiao Tung University (Government of The Republic of China)
• Original Assignee: National Chiao Tung University (Government of The Republic of China)
• Inventors: Chen, Meng-Chun, Bai, Mingsian R.
• Application Number:
  US11/592,185
• Publication Number:
  US 20080082323A1
• Time in Patent Office: Days
• US Class Current: 704/214
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 2218/04 Denoising

Abstract

A system that integrates various intelligent classification techniques and preprocessing algorithms is provided. A feature extracting unit receives audio signals and extracts audio features for identification by using various descriptors; a preprocessing unit normalized the data for data consistency; a classification unit classifying audio signals into several categories according to the audio features.

Claims

1. An intelligent classification system of sound signals comprising:

a feature extraction unit receiving a plurality of audio signals, and extracting a plurality of features from said audio signals by using a plurality of descriptors;

a data preprocessing unit coupling to said feature extraction unit, normalizing said features and generating a plurality of classification information; and

a classification unit coupling to said data preprocessing unit and grouping said audio signals to various kind of music according to said classification information.

2. The intelligent classification system of sound signals according to claim 1, further including an independent component analysis unit receiving said audio signals and separating said audio signals to a plurality of sound sources, thereby transferred to said feature extraction unit.

3. The intelligent classification system of sound signals according to claim 2, wherein said audio signals are mixed signals of a first acoustic wave and a second acoustic wave.

4. The intelligent classification system of sound signals according to claim 3, wherein said first acoustic wave is the creatures'"'"' sound signal.

5. The intelligent classification system of sound signals according to claim 4, wherein said second acoustic wave is the instruments'"'"' sound signal.

6. The intelligent classification system of sound signals according to claim 4, wherein said second acoustic wave is the environmental noises.

7. The intelligent classification system of sound signals according to claim 1, wherein said audio signals are mixed signals of the human'"'"'s sound signal and the instruments'"'"' sound signal.

8. The intelligent classification system of sound signals according to claim 7, wherein said feature extraction unit extracts said features from a spectral domain, a temporal domain and a statistical value.

9. The intelligent classification system of sound signals according to claim 8, wherein said feature extraction unit extracts said features in said spectral domain using a plurality of descriptors, wherein said descriptors comprises:

audio spectrum centroid, audio spectrum flatness, audio spectrum envelope, audio spectrum spread, harmonic spectrum centroid, harmonic spectrum deviation, harmonic spectrum variation, harmonic spectrum spread, spectrum centroid, linear predictive coding, Mel-scale frequency Cepstal coefficients, loudness, pitch, and autocorrelation.

10. The intelligent classification system of sound signals according to claim 8, wherein said feature extraction unit extracts said features in said temporal domain using a plurality of descriptors, wherein said descriptors comprises:

log attack time, temporal centroid and zero-crossing rate.

11. The intelligent classification system of sound signals according to claim 8, wherein said feature extraction unit extracts said features in said statistical value using a plurality of descriptors, wherein said descriptors comprises skewness and Kurtosis.

12. The intelligent classification system of sound signals according to claim 1, wherein said classification unit groups said audio signals by using nearest neighbor rule, artificial neural network, fuzzy neural network and hidden Markov model.

13. An intelligent classification method of sound signals comprising:

receiving a first audio signal and extracting a first group of feature variables by using a first independent component analysis unit;

normalizing said first group of feature variables and generating a plurality of classification items;

receiving a second audio signal and extracting a second group of feature variables;

normalizing said second group of feature variables and generating a plurality of classification information; and

using artificial intelligent algorithms to classify said second audio signal into said classification items, and storing said second audio signal into at least one memory.

14. The intelligent classification method of sound signals according to claim 13, further including receiving said second audio signal and separating said second audio signal into a plurality of sound components by using a second independent component analysis unit.

15. The intelligent classification method of sound signals according to claim 13, wherein said first audio signal is a training signal.

16. The intelligent classification method of sound signals according to claim 13, wherein said second audio signal is a mixed signal of a plurality of sound waves.

17. The intelligent classification method of sound signals according to claim 13, wherein said first group of feature variables are extracted from a spectral domain, a temporal domain and a statistical value.

18. The intelligent classification method of sound signals according to claim 13, wherein said second group of feature variables are extracted from a spectral domain, a temporal domain and a statistical value.

19. The intelligent classification method of sound signals according to claim 13, wherein said second audio signal is classified into said classification items by using nearest neighbor rule, artificial neural network, fuzzy neural network and hidden Markov model.