{"author":[{"first_name":"Joerg","full_name":"Schmalenstroeer, Joerg","id":"460","last_name":"Schmalenstroeer"},{"last_name":"Bartek","full_name":"Bartek, Markus","first_name":"Markus"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"oa":"1","abstract":[{"lang":"eng","text":"In this paper we propose to jointly consider Segmental Dynamic Time Warping and distance clustering for the unsupervised learning of acoustic events. As a result, the computational complexity increases only linearly with the dababase size compared to a quadratic increase in a sequential setup, where all pairwise SDTW distances between segments are computed prior to clustering. Further, we discuss options for seed value selection for clustering and show that drawing seeds with a probability proportional to the distance from the already drawn seeds, known as K-means++ clustering, results in a significantly higher probability of finding representatives of each of the underlying classes, compared to the commonly used draws from a uniform distribution. Experiments are performed on an acoustic event classification and an isolated digit recognition task, where on the latter the final word accuracy approaches that of supervised training."}],"quality_controlled":"1","year":"2011","status":"public","date_created":"2019-07-12T05:30:13Z","user_id":"460","citation":{"mla":"Schmalenstroeer, Joerg, et al. “Unsupervised Learning of Acoustic Events Using Dynamic Time Warping and Hierarchical K-Means++ Clustering.” <i>Interspeech 2011</i>, 2011.","ama":"Schmalenstroeer J, Bartek M, Haeb-Umbach R. Unsupervised learning of acoustic events using dynamic time warping and hierarchical K-means++ clustering. In: <i>Interspeech 2011</i>. ; 2011.","ieee":"J. Schmalenstroeer, M. Bartek, and R. Haeb-Umbach, “Unsupervised learning of acoustic events using dynamic time warping and hierarchical K-means++ clustering,” 2011.","apa":"Schmalenstroeer, J., Bartek, M., &#38; Haeb-Umbach, R. (2011). Unsupervised learning of acoustic events using dynamic time warping and hierarchical K-means++ clustering. <i>Interspeech 2011</i>.","bibtex":"@inproceedings{Schmalenstroeer_Bartek_Haeb-Umbach_2011, title={Unsupervised learning of acoustic events using dynamic time warping and hierarchical K-means++ clustering}, booktitle={Interspeech 2011}, author={Schmalenstroeer, Joerg and Bartek, Markus and Haeb-Umbach, Reinhold}, year={2011} }","short":"J. Schmalenstroeer, M. Bartek, R. Haeb-Umbach, in: Interspeech 2011, 2011.","chicago":"Schmalenstroeer, Joerg, Markus Bartek, and Reinhold Haeb-Umbach. “Unsupervised Learning of Acoustic Events Using Dynamic Time Warping and Hierarchical K-Means++ Clustering.” In <i>Interspeech 2011</i>, 2011."},"_id":"11889","title":"Unsupervised learning of acoustic events using dynamic time warping and hierarchical K-means++ clustering","department":[{"_id":"54"}],"main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2011/ScBaHa11-2.pdf"}],"language":[{"iso":"eng"}],"date_updated":"2023-10-26T08:10:44Z","type":"conference","publication":"Interspeech 2011"}