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MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis.

P. Buitelaar, I. Wood, S. Negi, M. Arcan, J. McCrae, A. Abele, C. Robin, V. Andryushechkin, H. Ziad, H. Sagha, M. Schmitt, B. Schuller, J. Sánchez-Rada, C. Iglesias, C. Navarro, A. Giefer, N. Heise, V. Masucci, F. Danza, C. Caterino, P. Smrz, M. Hradis, F. Povolný, M. Klimes, P. Matejka, and G. Tummarello. IEEE Trans. Multimedia, 20 (9): 2454-2465 (2018)

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Emotional Speech of Mentally and Physically Disabled Individuals: Introducing the EmotAsS Database and First Findings.S. Hantke, H. Sagha, N. Cummins, and B. Schuller. INTERSPEECH, page 3137-3141. ISCA, (2017)Collecting complex activity datasets in highly rich networked sensor environments.D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. Förster, G. Tröster, P. Lukowicz, D. Bannach, G. Pirkl, A. Ferscha and 9 other author(s). INSS, page 233-240. IEEE, (2010)Benchmarking classification techniques using the Opportunity human activity dataset.H. Sagha, S. Digumarti, J. del R. Millán, R. Chavarriaga, A. Calatroni, D. Roggen, and G. Tröster. SMC, page 36-40. IEEE, (2011)The effect of personality trait, age, and gender on the performance of automatic speech valence recognitionH. Sagha, J. Deng, and B. Schuller. 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), page 86--91. IEEE, (2017)Detecting anomalies to improve classification performance in opportunistic sensor networks.H. Sagha, J. del R. Millán, and R. Chavarriaga. PerCom Workshops, page 154-159. IEEE Computer Society, (2011)Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems.M. Kurz, G. Hölzl, A. Ferscha, H. Sagha, J. del R. Millán, and R. Chavarriaga. VTC Spring, page 1-5. IEEE, (2011)MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis.P. Buitelaar, I. Wood, S. Negi, M. Arcan, J. McCrae, A. Abele, C. Robin, V. Andryushechkin, H. Ziad, H. Sagha and 16 other author(s). IEEE Trans. Multimedia, 20 (9): 2454-2465 (2018)MixedEmotions: An Open-Source Toolbox for Multi-Modal Emotion AnalysisP. Buitelaar, I. Wood, S. Negi, M. Arcan, J. McCrae, A. Abele, C. Robin, V. Andryushechkin, H. Ziad, H. Sagha and 1 other author(s). IEEE Transactions on Multimedia, (2018)Implementing Gender-Dependent Vowel-Level Analysis for Boosting Speech-Based Depression Recognition.B. Vlasenko, H. Sagha, N. Cummins, and B. Schuller. INTERSPEECH, page 3266-3270. ISCA, (2017)Enhancing Speech-Based Depression Detection Through Gender Dependent Vowel-Level Formant Features.N. Cummins, B. Vlasenko, H. Sagha, and B. Schuller. AIME, volume 10259 of Lecture Notes in Computer Science, page 209-214. Springer, (2017)

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MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis.

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MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis.