Music Performers Classification by Using Multifractal Features: A Case Study
In this paper, we investigated the possibility to classify different performers playing the same melodies at the same manner being subjectively quite similar and very difficult to distinguish even for musically skilled persons. For resolving this problem we propose the use of multifractal (MF) analy...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2017-04-01
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| Series: | Archives of Acoustics |
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| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/1642 |
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| author | Natasa RELJIN David POKRAJAC |
| author_facet | Natasa RELJIN David POKRAJAC |
| author_sort | Natasa RELJIN |
| collection | DOAJ |
| description | In this paper, we investigated the possibility to classify different performers playing the same melodies at the same manner being subjectively quite similar and very difficult to distinguish even for musically skilled persons. For resolving this problem we propose the use of multifractal (MF) analysis, which is proven as an efficient method for describing and quantifying complex natural structures, phenomena or signals. We found experimentally that parameters associated to some characteristic points within the MF spectrum can be used as music descriptors, thus permitting accurate discrimination of music performers. Our approach is tested on the dataset containing the same songs performed by music group ABBA and by actors in the movie Mamma Mia. As a classifier we used the support vector machines and the classification performance was evaluated by using the four-fold cross-validation. The results of proposed method were compared with those obtained using mel-frequency cepstral coefficients (MFCCs) as descriptors. For the considered two-class problem, the overall accuracy and F-measure higher than 98% are obtained with the MF descriptors, which was considerably better than by using the MFCC descriptors when the best results were less than 77%. |
| format | Article |
| id | doaj-art-376069e5965249e1a977458ac60c197c |
| institution | DOAJ |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2017-04-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-376069e5965249e1a977458ac60c197c2025-08-20T03:14:38ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2017-04-0142210.1515/aoa-2017-0025Music Performers Classification by Using Multifractal Features: A Case StudyNatasa RELJIN0David POKRAJAC1University of ConnecticutDelaware State UniversityIn this paper, we investigated the possibility to classify different performers playing the same melodies at the same manner being subjectively quite similar and very difficult to distinguish even for musically skilled persons. For resolving this problem we propose the use of multifractal (MF) analysis, which is proven as an efficient method for describing and quantifying complex natural structures, phenomena or signals. We found experimentally that parameters associated to some characteristic points within the MF spectrum can be used as music descriptors, thus permitting accurate discrimination of music performers. Our approach is tested on the dataset containing the same songs performed by music group ABBA and by actors in the movie Mamma Mia. As a classifier we used the support vector machines and the classification performance was evaluated by using the four-fold cross-validation. The results of proposed method were compared with those obtained using mel-frequency cepstral coefficients (MFCCs) as descriptors. For the considered two-class problem, the overall accuracy and F-measure higher than 98% are obtained with the MF descriptors, which was considerably better than by using the MFCC descriptors when the best results were less than 77%.https://acoustics.ippt.pan.pl/index.php/aa/article/view/1642music classificationmultifractal analysissupport vector machinescross-validationmel-frequency cepstral coefficients |
| spellingShingle | Natasa RELJIN David POKRAJAC Music Performers Classification by Using Multifractal Features: A Case Study Archives of Acoustics music classification multifractal analysis support vector machines cross-validation mel-frequency cepstral coefficients |
| title | Music Performers Classification by Using Multifractal Features: A Case Study |
| title_full | Music Performers Classification by Using Multifractal Features: A Case Study |
| title_fullStr | Music Performers Classification by Using Multifractal Features: A Case Study |
| title_full_unstemmed | Music Performers Classification by Using Multifractal Features: A Case Study |
| title_short | Music Performers Classification by Using Multifractal Features: A Case Study |
| title_sort | music performers classification by using multifractal features a case study |
| topic | music classification multifractal analysis support vector machines cross-validation mel-frequency cepstral coefficients |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/1642 |
| work_keys_str_mv | AT natasareljin musicperformersclassificationbyusingmultifractalfeaturesacasestudy AT davidpokrajac musicperformersclassificationbyusingmultifractalfeaturesacasestudy |