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The goals of this project are the creation of a new dataset of sounds that belong to the domestic environment, called DomesticFSD2018, and to research on methods for the automatic classification of them. A Semi-Supervised approach is used to evaluate the possibility of exploiting samples that are not manually-verified. The purpose of this is to avoid the need of experts and save as many resources as possible in the validation process, that usually takes a lot of time and energies. The train set of DomesticFSD2018 is composed of a trustable (manually-verified) portion of data and a non-trustable (which has received no human validation and can be potentially inaccurate or mislabeled) one. A purely supervised learning approach is firstly followed, training models with only the trustable portion, and both trustable and non-trustable portions of data. Then the semi-supervised learning approach is experimented, using the models trained in the previous step to make predictions on non-trustable data. The samples predicted with the highest level of confidence are added to the train set, and finally, the classifier is re-trained using the updated and larger train set. In both cases, the technologies used are Support Vector Machines using MFCCs’ properties as input. The semi-supervised approach shows better results and allows us to add a considerable amount of non-trustable data to the trustable portion of the dataset.
", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2018-10-31", "publisher": "Zenodo", "resource_type": { "id": "publication-thesis", "title": { "de": "Abschlussarbeit", "en": "Thesis" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "Semi-supervised learning;" }, { "subject": "Domestic Environment;" }, { "subject": "Machine Learning;" }, { "subject": "Freesound Datasets Platform;" } ], "title": "Event Recognition of Domestic Sounds using Semi-Supervised Learning" }, "parent": { "access": { "owned_by": { "user": 54810 } }, "communities": { "default": "78c30cbc-1c1b-4ac2-a20b-7715387167e0", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2017-11-22T11:21:43.788681+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "78c30cbc-1c1b-4ac2-a20b-7715387167e0", "links": {}, "metadata": { "curation_policy": "Master thesis presented and approved by the Master in Sound and Music Computing of the Universitat Pompeu Fabra, Barcelona.
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