Audio Classification Based on Content Features
Audio classification is the process to classify different audio types according to contents. It is implemented in a large variety of real world problems, all classification applications allowed the target subjects to be viewed as a specific type of audio and hence, there is a variety in the audio ty...
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| Format: | Article |
| Language: | Arabic |
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College of Education for Women
2018-06-01
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| Series: | مجلة كلية التربية للبنات |
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| Online Access: | http://jcoeduw.uobaghdad.edu.iq/index.php/journal/article/view/1207 |
| _version_ | 1818143604706639872 |
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| author | اياد عبدالقهار عبدالسلام Ayad A. Abdulsalam |
| author_facet | اياد عبدالقهار عبدالسلام Ayad A. Abdulsalam |
| author_sort | اياد عبدالقهار عبدالسلام Ayad A. Abdulsalam |
| collection | DOAJ |
| description | Audio classification is the process to classify different audio types according to contents. It is implemented in a large variety of real world problems, all classification applications allowed the target subjects to be viewed as a specific type of audio and hence, there is a variety in the audio types and every type has to be treatedcarefully according to its significant properties.Feature extraction is an important process for audio classification. This workintroduces several sets of features according to the type, two types of audio (datasets) were studied. Two different features sets are proposed: (i) firstorder gradient feature vector, and (ii) Local roughness feature vector, the experimentsshowed that the results are competitive to those gotten from other popular methods inthis field, such as Zero Crossing Rate (ZCR), Amplitude Descriptor (AD), Short Time Energy (STE), and Volume (Vo). The test results indicated, that the attained averageaccuracy of classification is improved up to94.9232% for training set and 95.8666%for testing set.The classification performance of these two extracted featuresets is studied individually, and then they used together as one feature set. Theiroverall performance is investigated, the test results showed that the proposed methods give high classification rates for the audio. |
| first_indexed | 2024-12-11T11:34:19Z |
| format | Article |
| id | doaj.art-a0fb30904a8d4e39b69815f01d8e08b0 |
| institution | Directory Open Access Journal |
| issn | 1680-8738 2663-547X |
| language | Arabic |
| last_indexed | 2024-12-11T11:34:19Z |
| publishDate | 2018-06-01 |
| publisher | College of Education for Women |
| record_format | Article |
| series | مجلة كلية التربية للبنات |
| spelling | doaj.art-a0fb30904a8d4e39b69815f01d8e08b02022-12-22T01:08:48ZaraCollege of Education for Womenمجلة كلية التربية للبنات1680-87382663-547X2018-06-01285Audio Classification Based on Content Featuresاياد عبدالقهار عبدالسلام Ayad A. AbdulsalamAudio classification is the process to classify different audio types according to contents. It is implemented in a large variety of real world problems, all classification applications allowed the target subjects to be viewed as a specific type of audio and hence, there is a variety in the audio types and every type has to be treatedcarefully according to its significant properties.Feature extraction is an important process for audio classification. This workintroduces several sets of features according to the type, two types of audio (datasets) were studied. Two different features sets are proposed: (i) firstorder gradient feature vector, and (ii) Local roughness feature vector, the experimentsshowed that the results are competitive to those gotten from other popular methods inthis field, such as Zero Crossing Rate (ZCR), Amplitude Descriptor (AD), Short Time Energy (STE), and Volume (Vo). The test results indicated, that the attained averageaccuracy of classification is improved up to94.9232% for training set and 95.8666%for testing set.The classification performance of these two extracted featuresets is studied individually, and then they used together as one feature set. Theiroverall performance is investigated, the test results showed that the proposed methods give high classification rates for the audio.http://jcoeduw.uobaghdad.edu.iq/index.php/journal/article/view/1207Multimedia, Audio classification, Feature extraction, Short time energy, Local Roughness features, First Order Gradient Feature. |
| spellingShingle | اياد عبدالقهار عبدالسلام Ayad A. Abdulsalam Audio Classification Based on Content Features مجلة كلية التربية للبنات Multimedia, Audio classification, Feature extraction, Short time energy, Local Roughness features, First Order Gradient Feature. |
| title | Audio Classification Based on Content Features |
| title_full | Audio Classification Based on Content Features |
| title_fullStr | Audio Classification Based on Content Features |
| title_full_unstemmed | Audio Classification Based on Content Features |
| title_short | Audio Classification Based on Content Features |
| title_sort | audio classification based on content features |
| topic | Multimedia, Audio classification, Feature extraction, Short time energy, Local Roughness features, First Order Gradient Feature. |
| url | http://jcoeduw.uobaghdad.edu.iq/index.php/journal/article/view/1207 |
| work_keys_str_mv | AT ạyạdʿbdạlqhạrʿbdạlslạmayadaabdulsalam audioclassificationbasedoncontentfeatures |