Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations
The present paper focuses on adaptive audio detection, segmentation and classification techniques in audio broadcasting content, dedicated mainly to voice data. The suggested framework addresses a real case scenario encountered in media services and especially radio streams, aiming to fulfill divers...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Knowledge |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-9585/2/3/20 |
| _version_ | 1797464868526227456 |
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| author | Rigas Kotsakis Charalampos Dimoulas |
| author_facet | Rigas Kotsakis Charalampos Dimoulas |
| author_sort | Rigas Kotsakis |
| collection | DOAJ |
| description | The present paper focuses on adaptive audio detection, segmentation and classification techniques in audio broadcasting content, dedicated mainly to voice data. The suggested framework addresses a real case scenario encountered in media services and especially radio streams, aiming to fulfill diverse (semi-) automated indexing/annotation and management necessities. In this context, aggregated radio content is collected, featuring small input datasets, which are utilized for adaptive classification experiments, without searching, at this point, for a generic pattern recognition solution. Hierarchical and hybrid taxonomies are proposed, firstly to discriminate voice data in radio streams and thereafter to detect single speaker voices, and when this is the case, the experiments proceed into a final layer of gender classification. It is worth mentioning that stand-alone and combined supervised and clustering techniques are tested along with multivariate window tuning, towards the extraction of meaningful results based on overall and partial performance rates. Furthermore, the current work via data augmentation mechanisms contributes to the formulation of a dynamic Generic Audio Classification Repository to be subjected, in the future, to adaptive multilabel experimentation with more sophisticated techniques, such as deep architectures. |
| first_indexed | 2024-03-09T18:14:16Z |
| format | Article |
| id | doaj.art-ee3b721d736144a7953d10805796227f |
| institution | Directory Open Access Journal |
| issn | 2673-9585 |
| language | English |
| last_indexed | 2024-03-09T18:14:16Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Knowledge |
| spelling | doaj.art-ee3b721d736144a7953d10805796227f2023-11-24T08:55:07ZengMDPI AGKnowledge2673-95852022-07-012334736410.3390/knowledge2030020Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation AutomationsRigas Kotsakis0Charalampos Dimoulas1Department of Information and Electronic Engineering, International Hellenic University, 57001 Thessaloniki, GreeceSchool of Journalism & Mass Communications, Aristotle University, 54124 Thessaloniki, GreeceThe present paper focuses on adaptive audio detection, segmentation and classification techniques in audio broadcasting content, dedicated mainly to voice data. The suggested framework addresses a real case scenario encountered in media services and especially radio streams, aiming to fulfill diverse (semi-) automated indexing/annotation and management necessities. In this context, aggregated radio content is collected, featuring small input datasets, which are utilized for adaptive classification experiments, without searching, at this point, for a generic pattern recognition solution. Hierarchical and hybrid taxonomies are proposed, firstly to discriminate voice data in radio streams and thereafter to detect single speaker voices, and when this is the case, the experiments proceed into a final layer of gender classification. It is worth mentioning that stand-alone and combined supervised and clustering techniques are tested along with multivariate window tuning, towards the extraction of meaningful results based on overall and partial performance rates. Furthermore, the current work via data augmentation mechanisms contributes to the formulation of a dynamic Generic Audio Classification Repository to be subjected, in the future, to adaptive multilabel experimentation with more sophisticated techniques, such as deep architectures.https://www.mdpi.com/2673-9585/2/3/20audio semanticscontent analysisradio broadcasting |
| spellingShingle | Rigas Kotsakis Charalampos Dimoulas Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations Knowledge audio semantics content analysis radio broadcasting |
| title | Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations |
| title_full | Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations |
| title_fullStr | Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations |
| title_full_unstemmed | Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations |
| title_short | Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations |
| title_sort | extending radio broadcasting semantics through adaptive audio segmentation automations |
| topic | audio semantics content analysis radio broadcasting |
| url | https://www.mdpi.com/2673-9585/2/3/20 |
| work_keys_str_mv | AT rigaskotsakis extendingradiobroadcastingsemanticsthroughadaptiveaudiosegmentationautomations AT charalamposdimoulas extendingradiobroadcastingsemanticsthroughadaptiveaudiosegmentationautomations |