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• W2215208035 abstract "In service engineering it is important to estimate when and what a worker did, because they include crucial evidences to improve service quality and working environments. For Service Operation Estimation (SOE), acoustic information is one of useful and key modalities; particularly environmental or background sounds include effective cues. This paper focuses on two aspects: (1) extracting powerful and robust acoustic features by using stacked-denoising-autoencoder and bag-of-feature techniques, and (2) investigating a multi-modal SOE scheme by combining the audio features and the other sensor data as well as non-sensor information. We conducted evaluation experiments using multi-modal data recorded in a restaurant. We improved SOE performance in comparison to conventional acoustic features, and effectiveness of our multimodal SOE scheme is also clarified." @default.
• W2215208035 created "2016-06-24" @default.
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• W2215208035 date "2015-08-01" @default.
• W2215208035 modified "2023-09-25" @default.
• W2215208035 title "Multi-modal service operation estimation using DNN-based acoustic bag-of-features" @default.
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• W2215208035 doi "https://doi.org/10.1109/eusipco.2015.7362793" @default.
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