Investigating Auditory Human-Machine Interaction: Analysis and Classification of Sounds Commonly Used by Consumer Devices
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Investigating Auditory Human-Machine Interaction: Analysis and Classification of Sounds Commonly Used by Consumer Devices. / Drossos, Konstantinos; Kotsakis, Rigas; Pappas, Panos; Kalliris, George; Floros, Andreas.
Audio Engineering Society Convention 134. AES Audio Engineering Society, 2013. 8812.Tutkimustuotos ›
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TY - GEN
T1 - Investigating Auditory Human-Machine Interaction: Analysis and Classification of Sounds Commonly Used by Consumer Devices
AU - Drossos, Konstantinos
AU - Kotsakis, Rigas
AU - Pappas, Panos
AU - Kalliris, George
AU - Floros, Andreas
PY - 2013/5
Y1 - 2013/5
N2 - Many common consumer devices use a short sound indication for declaring various modes of their functionality, such as the start and the end of their operation. This is likely to result in an intuitive auditory human-machine interaction, imputing a semantic content to the sounds used. In this paper we investigate sound patterns mapped to "Start" and "End" of operation manifestations and explore the possibility such semantics’ perception to be based either on users’ prior auditory training or on sound patterns that naturally convey appropriate information. To this aim, listening and machine learning tests were conducted. The obtained results indicate a strong relation between acoustic cues and semantics along with no need of prior knowledge for message conveyance.
AB - Many common consumer devices use a short sound indication for declaring various modes of their functionality, such as the start and the end of their operation. This is likely to result in an intuitive auditory human-machine interaction, imputing a semantic content to the sounds used. In this paper we investigate sound patterns mapped to "Start" and "End" of operation manifestations and explore the possibility such semantics’ perception to be based either on users’ prior auditory training or on sound patterns that naturally convey appropriate information. To this aim, listening and machine learning tests were conducted. The obtained results indicate a strong relation between acoustic cues and semantics along with no need of prior knowledge for message conveyance.
M3 - Conference contribution
BT - Audio Engineering Society Convention 134
PB - AES Audio Engineering Society
ER -