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While many recognition tasks have traditionally employed inertial sensors, acoustic-based methods offer the benefit of capturing rich contextual information, which can be useful when discriminating complex activities. Given the emergence of deep learning techniques and leveraging new, large-scale multimedia datasets, this paper revisits the opportunity of training audio-based classifiers without the onerous and time-consuming task of annotating audio data. We propose a framework for audio-based activity recognition that can make use of millions of embedding features from public online video sound clips. Based on the combination of oversampling and deep learning approaches, our framework does not require further feature processing or outliers filtering as in prior work. We evaluated our approach in the context of Activities of Daily Living (ADL) by recognizing 15 everyday activities with 14 participants in their own homes, achieving 64.2% and 83.6% averaged within-subject accuracy in terms of top-1 and top-3 classification respectively. Individual class performance was also examined in the paper to further study the co-occurrence characteristics of the activities and the robustness of the framework.<\/jats:p>","DOI":"10.1145\/3314404","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T11:57:40Z","timestamp":1554206260000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":78,"title":["Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos"],"prefix":"10.1145","volume":"3","author":[{"given":"Dawei","family":"Liang","sequence":"first","affiliation":[{"name":"University of Texas at Austin, Austin, USA"}]},{"given":"Edison","family":"Thomaz","sequence":"additional","affiliation":[{"name":"University of Texas at Austin, Austin, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,3,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. 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