Bibliography

ADudikW19

Alekh Agarwal, Miroslav Dudík, and Zhiwei Steven Wu. Fair regression: quantitative definitions and reduction-based algorithms. In International Conference on Machine Learning, 120–129. PMLR, 2019.

APC+17

Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Simone Hantke, Jouni Pohjalainen, Gil Keren, and Björn W. Schuller. CAST a database: rapid targeted large-scale big data acquisition via small-world modelling of social media platforms. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), 340–345. 2017. URL: https://doi.org/10.1109/ACII.2017.8273622.

FSRE07

Johnny RJ Fontaine, Klaus R Scherer, Etienne B Roesch, and Phoebe C Ellsworth. The world of emotions is not two-dimensional. Psychological science, 18(12):1050–1057, 2007. URL: https://doi.org/10.1111/j.1467-9280.2007.02024.x.

GFSS16

Christelle Gillioz, Johnny RJ Fontaine, Cristina Soriano, and Klaus R Scherer. Mapping emotion terms into affective space: Further evidence for a four-dimensional structure. Swiss Journal of Psychology, 75(3):141, 2016. URL: https://www.researchgate.net/profile/Christelle-Gillioz/publication/304184175_Mapping_Emotion_Terms_into_Affective_Space_Further_Evidence_for_a_Four-Dimensional_Structure/links/5770e03b08ae842225aad306/Mapping-Emotion-Terms-into-Affective-Space-Further-Evidence-for-a-Four-Dimensional-Structure.pdf.

HSS+12

Holger Hoffmann, Andreas Scheck, Timo Schuster, Steffen Walter, Kerstin Limbrecht, Harald C. Traue, and Henrik Kessler. Mapping discrete emotions into the dimensional space: An empirical approach. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 3316–3320. 2012. URL: https://www.researchgate.net/profile/Harald-Traue/publication/234063387_Mapping_discrete_emotions_into_the_dimensional_space_An_empirical_approach/links/545257fa0cf2cf516479c6e2/Mapping-discrete-emotions-into-the-dimensional-space-An-empirical-approach.pdf.

JP21

Mimansa Jaiswal and Emily Mower Provost. Best practices for noise-based augmentation to improve the performance of emotion recognition "in the wild". arXiv preprint arXiv:2104.08806, 2021. URL: https://arxiv.org/abs/2104.08806.

JSV09

Marco Jeub, Magnus Schäfer, and Peter Vary. A binaural room impulse response database for the evaluation of dereverberation algorithms. In Proceedings of International Conference on Digital Signal Processing (DSP), 1–4. Santorini, Greece, July 2009. IEEE, IET, EURASIP, IEEE.

MMS+21

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM Comput. Surv., jul 2021. doi:10.1145/3457607.

SGCP15

David Snyder, Guoguo Guoguo Chen, and Daniel Povey. MUSAN: a music, speech, and noise corpus. arXiv preprint arXiv:1510.08484, 2015. URL: https://arxiv.org/abs/1510.08484.

VT17

Gyanendra K Verma and Uma Shanker Tiwary. Affect representation and recognition in 3D continuous valence–arousal–dominance space. Multimedia Tools and Applications, 76(2):2159–2183, 2017. URL: https://www.researchgate.net/profile/Gyanendra-Verma/publication/284724383_Affect_Representation_and_Recognition_in_3D_Continuous_Valence-Arousal-Dominance_Space/links/5b0f85ca4585157f872485be/Affect-Representation-and-Recognition-in-3D-Continuous-Valence-Arousal-Dominance-Space.pdf.

WGH+06

Jimi YC Wen, Nikolay D Gaubitch, Emanuel AP Habets, Tony Myatt, and Patrick A Naylor. Evaluation of speech dereverberation algorithms using the mardy database. In in Proc. Intl. Workshop Acoust. Echo Noise Control (IWAENC. 2006. URL: https://www.researchgate.net/profile/Peter-Vary/publication/224576432_A_binaural_room_impulse_response_database_for_the_evaluation_of_dereverberation_algorithms/links/00b4952a97efc81c15000000/A-binaural-room-impulse-response-database-for-the-evaluation-of-dereverberation-algorithms.pdf.

ZHML19

Jie M. Zhang, Mark Harman, Lei Ma, and Yang Liu. Machine Learning Testing: Survey, Landscapes and Horizons. arXiv:1906.10742 [cs, stat], 2019. URL: http://arxiv.org/abs/1906.10742.