Features for Audio and Music Classification
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1 Features for Audio and Music Classification Martin F. McKinney and Jeroen Breebaart Auditory and Multisensory Perception, Digital Signal Processing Group Philips Research Laboratories Eindhoven, The Netherlands
2 Introduction Wanted: automatic audio and music classifier Previous work: Typical method: Feature extraction followed by classification Specific method of classification is not always crucial i.e., features are the limiting factor Temporal properties of audio are important for classification and summarization Our focus here is on features for audio classification and their temporal properties 2
3 Method: General Compare classification performance of four feature sets: Standard low-level signal parameters Mel-frequency cepstral coefficients (MFCC) Psychoacoustic features Auditory filterbank temporal envelope Include statistics of feature temporal behavior as additional features Evaluate classification using a multivariate Gaussian framework (Quadratic Discriminate Analysis - QDA) 3
4 Method: Feature extraction 743-ms analysis frame 23-ms subframes Feature extraction Subframe feature vectors Spectral feature modeling Spectral Feature model 0 Hz 1-2 Hz 3-15 Hz Hz Feature selection (9 best for maximum prediction training data) Final feature vector 4
5 Method: Classification Classification tasks Five class general audio classification Classical music (35), popular music (188), speech (31), background noise (25), crowd noise (31) Seven class music genre classification Jazz (38), Folk (23), Electronica (27), R&B (43), Rock (37), Reggae (11), Vocal (9) QDA training and cross-validation with the.632+ bootstrap method 5
6 Results: Standard Low Level features Feature ranking: General Audio, Music Genre 1. RMS level 3, 3 8 7, 9 2. Spectral centroid 3. Bandwidth 4. Zero crossing rate 5. Spectral roll-off freq 6. Band energy ratio 7. Delta spectrum mag. 8. Pitch 9. Pitch strength DC 6, 7 4 1, 2 2, 6 5, Hz 3-15 Hz 4, Hz 6
7 Results: Standard Low Level features Classification with 9 best features General Audio (86±4%) Music Genre (61±11%) Real Class Clas Pop Spch Ns e Crwd 0.98 ± ± ± ± ±0.02 Clas Pop Spch Ns e Crwd Jazz Folk Elct R&B Rock Regg Vocl 0.64 ± ± ± ± ± ± ±0.22 Jazz Folk Elct R&B Rock Regg Vocl Classification Result 7
8 Results: MFCC features Feature ranking: General Audio, Music Genre 1. MFCC 0 3, 2 2, MFCC 1 3. MFCC 2 4. MFCC 3 5. MFCC 4 6. MFCC 5 7. MFCC 6 8. MFCC 7 9. MFCC MFCC MFCC MFCC MFCC 12 DC 1, 4 5, , Hz 3-15 Hz Hz 4 8
9 Results: MFCC features Classification with 9 best features General Audio (92±3%) Music Genre (65±10%) Real Class Clas Pop Spch Ns e Crwd 0.89 ± ± ± ± ±0.02 Clas Pop Spch Ns e Crwd Jazz Folk Elct R&B Rock Regg Vocl 0.68 ± ± ± ± ± ± ±0.2 Jazz Folk Elct R&B Rock Regg Vocl Classification Result 9
10 Results: Psychoacoustic features Feature ranking: General Audio, Music Genre DC 1-2 Hz 3-15 Hz Hz 1. Roughness 3, 2 N/A N/A N/A 2. Roughness Std. Dev. 7 N/A N/A N/A 3. Loudness 4, 5 8 6, 6 5, 4 4. Sharpness 2, 1 9, 7 1, 3 8, 9 10
11 Results: Psychoacoustic features Classification with 9 best features General Audio (92±3%) Music Genre (62±10%) Real Class Clas Pop Spch Ns e Crwd 0.94 ± ± ± ± ±0.03 Clas Pop Spch Ns e Crwd Jazz Folk Elct R&B Rock Regg Vocl 0.63 ± ± ± ± ± ± ±0.2 Jazz Folk Elct R&B Rock Regg Vocl Classification Result 11
12 Results: AFTE features Feature ranking: General Audio, Music Genre 1. AFTE 1 (Fc = 26 Hz) 7, 6 N/A N/A 2. AFTE 2 (Fc = 88 Hz) 3. AFTE 3 (Fc = 164 Hz) 4. AFTE 4 (Fc = 258 Hz) 7. AFTE 7 (Fc = 703 Hz) 8. AFTE 8 (Fc = 927 Hz) 9. AFTE 9 (Fc = 1206 Hz) 12. AFTE 12 (Fc = 2514 Hz) 16. AFTE 16 (Fc = 6279 Hz) 17. AFTE 17 (Fc = 7848 Hz) 18. AFTE 18 (Fc = 9795 Hz) DC 1 1, , Hz Hz N/A Hz N/A N/A N/A N/A N/A 2 12
13 Results: AFTE features Classification with 9 best features General Audio (93±2%) Music Genre (74±9%) Real Class Clas Pop Spch Ns e Crwd 0.94 ± ± ± ± ±0.03 Clas Pop Spch Ns e Crwd Jazz Folk Elct R&B Rock Regg Vocl 0.81 ± ± ± ± ± ± ±0.16 Jazz Folk Elct R&B Rock Regg Vocl Classification Result 13
14 Results Summary SLL MFCC PA AFTE General Audio 86±4% 92±3% 92±3% 93±2% Music Genre 61±11% 65±10% 62±10% 74±9% 14
15 Conclusions Classification based on features from an auditory model (AFTE) is better than that from other standard feature sets. Temporal modulations of features are important for audio and music classification. Feature development can improve audio and music classification. 15
16
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