Retrieval of Drum Samples by High-Level Descriptors

TitleRetrieval of Drum Samples by High-Level Descriptors
Publication TypeMaster Thesis
Year of Publication2017
AuthorsArredondo Garrido, J.
AbstractSystems that manage audio databases for sound description and retrieval could be really useful within a context of music production. In this thesis, one of these systems has been created using classification techniques, by the application of learning algorithms and audio feature selections on two datasets. One of them is composed by commercial sounds and the other one is formed with sounds from an online repository of Creative Commons audio content. Due to the fact that drums are a fundamental element on most of the musical genres nowadays, it is the family of instruments chosen to train and test the presented classification models. Research is focused on finding generalist drum instrument class and category models, understanding category as the source nature of the sample (acoustic or digital). Generalization on these models lead us to be able to classify different drum sound datasets, achieving good model and prediction accuracies. Combining these models with an annotation of our datasets with specific values of audio high-level descriptors (Brightness, Hardness, Roughness and Depth), a drum samples retrieval tool could be created and would open new possibilities for database management within a music production framework.
Final publicationhttps://doi.org/10.5281/zenodo.1112353
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