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Authors: Mostafa Al Masum Shaikh 1 ; Keikichi Hirose 1 and Helmut Prendinger 2

Affiliations: 1 University of Tokyo, Japan ; 2 National Institute of Informatics, Japan

Keyword(s): Acoustic Event Detection, Context Awareness, Activity Detection, Sound Cues, Auditory Scene Analysis, Commonsense Knowledge, Ambient Communication, Life-Logging.

Related Ontology Subjects/Areas/Topics: Applications ; Audio and Speech Processing ; Design and Implementation of Signal Processing Systems ; Digital Signal Processing ; Human-Machine Interface ; Multimedia ; Multimedia Signal Processing ; Multimedia Systems and Applications ; Pattern Recognition ; Software Engineering ; Telecommunications

Abstract: Detecting or inferring human activity (e.g., an outdoor activity) by analyzing sensor data is often inaccurate, insufficient, difficult, and expensive. Therefore, this paper explains an approach to infer human activity and location considering the environmental sound cues and commonsense knowledge of everyday objects usage. Our system uses mel-frequency cepstral coefficients (MFCC) and their derivatives as features, and continuous density hidden Markov models (HMM) as acoustic models. Our work differs from others in three key ways. First, we utilize both indoor and outdoor environmental sound cues which are annotated according to the objects pertaining to the sound samples to build the idea regarding sounds and the objects which produce that particular sound. Second, use of portable microphone instead of having a fixed setup of an array of microphones to capture environmental sound we can also infer outdoor environments like being on the road, in a train station, etc., which previous research was limited to perform. Thirdly, our model is easy to incorporate new set of activities for further needs by adding more appropriately annotated sound clips and re-training of the HMM based recognizer. A perceptual test is made to study the human accuracy in the task and to obtain a baseline for the assessment of the performance of the system. Though the direct comparison of the system’s performance to human performance is somewhat worse but the preliminary results are encouraging with the accuracy rate for outdoor and indoor sound categories for activities being above 67% and 61% respectively. (More)

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Paper citation in several formats:
Al Masum Shaikh, M.; Hirose, K. and Prendinger, H. (2009). CONTEXT AWARENESS USING ENVIRONMENTAL SOUND CUES AND COMMONSENSE KNOWLEDGE. In Proceedings of the International Conference on Signal Processing and Multimedia Applications (ICETE 2009) - SIGMAP; ISBN 978-989-674-007-8, SciTePress, pages 193-196. DOI: 10.5220/0002230901930196

@conference{sigmap09,
author={Mostafa {Al Masum Shaikh}. and Keikichi Hirose. and Helmut Prendinger.},
title={CONTEXT AWARENESS USING ENVIRONMENTAL SOUND CUES AND COMMONSENSE KNOWLEDGE},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications (ICETE 2009) - SIGMAP},
year={2009},
pages={193-196},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002230901930196},
isbn={978-989-674-007-8},
}

TY - CONF

JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications (ICETE 2009) - SIGMAP
TI - CONTEXT AWARENESS USING ENVIRONMENTAL SOUND CUES AND COMMONSENSE KNOWLEDGE
SN - 978-989-674-007-8
AU - Al Masum Shaikh, M.
AU - Hirose, K.
AU - Prendinger, H.
PY - 2009
SP - 193
EP - 196
DO - 10.5220/0002230901930196
PB - SciTePress