{"641956":{"#nid":"641956","#data":{"type":"event","title":"Ph.D. Proposal Oral Exam - You Wang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003E\u003Cem\u003EAttention-based Convolutional Neural Network Model and Its Combination with Few-shot Learning for Audio Classification\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u0026nbsp; \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Anderson, Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Rozell, Chair\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Davenport\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003EThe objective of the proposed research is to improve the performance of environmental sound and acoustic scene classification using an attention mechanism combined with few-shot learning to deal with data scarcity. The proposed work seeks to extract salient feature regions by introducing a new light-weight attention-based CNN model and combine it with few-shot learning approaches, especially prototypical networks to overcome the limitation of training data.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Attention-based Convolutional Neural Network Model and Its Combination with Few-shot Learning for Audio Classification"}],"uid":"28475","created_gmt":"2020-12-08 19:31:08","changed_gmt":"2020-12-08 19:31:08","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2020-12-15T10:00:00-05:00","event_time_end":"2020-12-15T12:00:00-05:00","event_time_end_last":"2020-12-15T12:00:00-05:00","gmt_time_start":"2020-12-15 15:00:00","gmt_time_end":"2020-12-15 17:00:00","gmt_time_end_last":"2020-12-15 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434371","name":"ECE Ph.D. Proposal Oral Exams"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}