2015
Extraction of Key Segments from Day-Long Sound Data
HCI International 2015 - Posters’ Extended Abstracts (Part I), CCIS 528
- ,
- ,
- Volume
- 528
- Number
- First page
- 620
- Last page
- 626
- Language
- English
- Publishing type
- Part of collection (book)
- DOI
- 10.1007/978-3-319-21380-4_105
- Publisher
- SPRINGER-VERLAG BERLIN
We propose a method to extract particular sound segments from the sound recorded during the course of a day in order to provide sound segments that can be used to facilitate memory. To extract important parts of the sound data, the proposed method utilizes human behavior based on a multisensing approach. To evaluate the performance of the proposed method, we conducted experiments using sound, acceleration, and global positioning system data collected by five participants for approximately two weeks. The experimental results are summarized as follows: (1) various sounds can be extracted by dividing a day into scenes using the acceleration data; (2) sound recorded in unusual places is preferable to sound recorded in usual places; and (3) speech is preferable to nonspeech sound.
- Link information
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- DOI
- https://doi.org/10.1007/978-3-319-21380-4_105
- DBLP
- https://dblp.uni-trier.de/rec/conf/hci/KasaiHA15
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000377404100105&DestApp=WOS_CPL
- URL
- http://dblp.uni-trier.de/db/conf/hci/hci2015-27.html#conf/hci/KasaiHA15
- ID information
-
- DOI : 10.1007/978-3-319-21380-4_105
- ISSN : 1865-0929
- eISSN : 1865-0937
- DBLP ID : conf/hci/KasaiHA15
- Web of Science ID : WOS:000377404100105