RESEARCH AND OPEN SOURCE
We value confidentiality, especially that of our customers. But we do not sell black-box secret technology where there is no published evidence of functionality. All of our core technology is based on well established tools, which are partially available freely and open to the research community – and which have been used in several renowned benchmarks and competitions (e.g. ComParE).
It is, of course, a long way from a research tool to a product, and there are integral parts of our products which go beyond the freely available tools and are strictly confidential. However, a strong link to academia and the transparency of evaluations and benchmarks of our technology is very important for us.
Thus, at audEERING, cutting edge research has a high priority in order to make our current technologies even more innovative and intelligent. We collaborate with world-leading research institutions to always deliver state-of-the-art technology. In turn, we are committed to returning resources to the research community by publishing our findings and providing research versions of our tools.
See below for a list of our current research tools and publications.
Dedicated commercial research projects
You are interested in exploring and developing new, innovative audio analysis solutions for your products, but don’t have your own R&D team or as missing the right expertise?
We can conduct custom research projects of any length for you. Contact us and tell us about your requirements and ideas and we will make you a good offer.
Academic research projects
Given our academic research roots, we continue working and contributing to academic research. If you are looking for an audio, speech, emotion, or music expert project partner from the industry for your consortium, be sure to talk to us!
The world-famous openSMILE speech analysis toolkit is now maintained by audEERING. It provides a technically solid and scientifically well evaluated core for audEERING’s proprietary audio and speech analysis technology.
GeMAPS – standard acoustic paramater recommendation
Agreed upon by many leading scientists, including psychologists, linguists, voice researchers, and engineers, the Geneva Minimalistic Acoustic Parameter Set (GeMAPS) sets baseline standards for audio research related to the human voice. A draft recommendation led by TUM was submitted to IEEE Transactions on Affective Computing for publication. audEERING supports the standard by hosting a wiki for discussions and updates and providing configuration files for extracting the parameters with openSMILE. More information will follow on this page shortly.
B. Schuller, S. Steidl, A. Batliner, P. B. Marschik, H. Baumeister, F. Dong, … & C. Einspieler, “The Interspeech 2018 Computational Paralinguistics Challenge: Atypical & self-assessed affect, crying & heart beats,” Proc. of INTERSPEECH, Hyderabad, India, 2018.
A. Triantafyllopoulos, H. Sagha, F. Eyben, B. Schuller, “audEERING’s approach to the One-Minute-Gradual Emotion Challenge,” arXiv preprint arXiv:1805.01222
J. Deng, B. Schuller, “Detecting Vocal Irony,” in Language Technologies for the Challenges of the Digital Age: 27th International Conference, GSCL 2017, Vol. 10713, p. 11, Springer
H. J. Vögel, C. Süß, T. Hubregtsen, V. Ghaderi, R. Chadowitz, E. André, … & B. Huet, “Emotion-awareness for intelligent vehicle assistants: a research agenda,” in Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems, pp. 11-15, ACM
G. Hagerer, N. Cummins, F. Eyben, B. Schuller, “Robust Laughter Detection for Wearable Wellbeing Sensing,” in Proceedings of the 2018 International Conference on Digital Health, pp. 156-157, ACM
J. Deng, F. Eyben, B. Schuller, F. Burkhardt, “Deep neural networks for anger detection from real life speech data,” in Proc. of 2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 1-6, IEEE
E. Marchi, F. Vesperini, S. Squartini, B. Schuller, “Deep recurrent neural network-based autoencoders for acoustic novelty detection,” in Computational intelligence and neuroscience, 2017
G. Hagerer, N. Cummins, F. Eyben, B. Schuller, “Did you laugh enough today? – Deep Neural Networks for Mobile and Wearable Laughter Trackers,” in Proc. Interspeech 2017, pp. 2044-2045
B. Schuller, “Automatic speaker analysis 2.0: Hearing the bigger picture,” in Proc. of 2017 International Conference onSpeech Technology and Human-Computer Dialogue (SpeD), pp. 1-6, IEEE
J. Böhm, F. Eyben, M. Schmitt, H. Kosch, B. Schuller, “Seeking the SuperStar: Automatic assessment of perceived singing quality,” in Proc. of 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1560-1569, IEEE
G. Hagerer, V. Pandit, F. Eyben, B. Schuller, “Enhancing LSTM RNN-Based Speech Overlap Detection by Artificially Mixed Data,” in Proc. 2017 AES International Conference on Semantic Audio
Y. Zhang, F. Weninger, B. Liu, M. Schmitt, F. Eyben, B. Schuller, “A Paralinguistic Approach To Speaker Diarisation: Using Age, Gender, Voice Likability and Personality Traits,” in Proc. 2017 ACM Conference on Multimedia, Mountain View, California, USA, pp. 387-392
N. Cummins, S. Amiriparian, G. Hagerer, A. Batliner, S. Steidl, B. Schuller, “An Image-based Deep Spectrum Feature Representation for the Recognition of Emotional Speech,” in Proc. 2017 ACM Conference on Multimedia, Mountain View, California, USA, pp. 478-484
K. Qian, C. Janott, J. Deng, C. Heiser, W. Hohenhorst, M. Herzog, N. Cummins, B. Schuller, “Snore sound recognition: On wavelets and classifiers from deep nets to kernels,” in Proc. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3737-3740
H. Sagha, J. Deng, B. Schuller, “The effect of personality trait, age, and gender on the performance of automatic speech valence recognition,” in Proc. 7th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2017), San Antonio, Texas, AAAC, IEEE, October 2017
G. Hagerer, F. Eyben, H. Sagha, D. Schuller, B. Schuller, “VoicePlay – An Affective Sports Game Operated by Speech Emotion Recognition based on the Component Process Model”, accepted demo at 7th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2017), San Antonio, Texas, AAAC, IEEE, October 2017
F. Eyben, M. Unfried, G. Hagerer, B. Schuller, “Automatic Multi-lingual Arousal Detection from Voice Applied to Real Product Testing Applications,” in Proc. 42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017), New Orleans, LA, IEEE
E. Marchi, F. Eyben, G. Hagerer, B. Schuller, “Real-time Tracking of Speakers’ Emotions, States, and Traits on Mobile Platforms,” in Proc. INTERSPEECH 2016, San Francisco, Califorina, USA, pp. 1182-1183
S. Hantke, E. Marchi, B. Schuller, “Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification,” in Proc. LREC 2016
F. Eyben, B. Huber, E. Marchi, D. Schuller, B. Schuller, “Real-time Robust Recognition of Speakers’ Emotions and Characteristics on Mobile Platforms,” in Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2015), Xi’an, P. R. China, AAAC, IEEE, pp. 778-780, September 2015
S. Hantke, T. Appel, F. Eyben, B. Schuller, “iHEARu-PLAY: Introducing a game for crowdsourced data collection for affective computing,” in Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2015), Xi’an, P. R. China, AAAC, IEEE, pp. 891-897, September 2015
A. Metallinou, M. Wöllmer, A. Katsamanis, F. Eyben, B. Schuller, S. Narayanan, “Context-Sensitive Learning for Enhanced Audiovisual Emotion Classification (Extended Abstract),” in Proc. of ACII 2015, Xi’an, China, invited for the Special Session on Most Influential Articles in IEEE Transactions on Affective Computing
B. Schuller, B. Vlasenko, F. Eyben, M. Wöllmer, A. Stuhlsatz, A. Wendemuth, G. Rigoll, “Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies (Extended Abstract),” in Proc. of ACII 2015, Xi’an, China, invited for the Special Session on Most Influential Articles in IEEE Transactions on Affective Computing
M. Schröder, E. Bevacqua, R. Cowie, F. Eyben, H. Gunes, D. Heylen, M. ter Maat, G. McKeown, S. Pammi, M. Pantic, C. Pelachaud, B. Schuller, E. de Sevin, M. Valstar, M. Wöllmer, “Building Autonomous Sensitive Artificial Listeners (Extended Abstract),” in Proc. of ACII 2015, Xi’an, China, invited for the Special Session on Most Influential Articles in IEEE Transactions on Affective Computing
F. Eyben, K. Scherer, B. Schuller, J. Sundberg, E. Andre, C. Busso, L. Devillers, J. Epps, P. Laukka, S. Narayanan, K. Truong, “The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing,” IEEE Transactions on Affective Computing, 2015
B. Schuller, “Speech Analysis in the Big Data Era,” in Proc. of the 18th International Conference on Text, Speech and Dialogue, TSD 2015, Lecture Notes in Artificial Intelligence (LNAI), Springer, September 2015, Satellite event of INTERSPEECH 2015
S. Feraru, D. Schuller, B. Schuller, “Cross-Language Acoustic Emotion Recognition: An Overview and Some Tendencies,” in Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2015), (Xi’an, P. R. China), AAAC, IEEE, pp. 125-131, September 2015
F. Eyben, F. Weninger, B. Schuller, “Affect recognition in real-life acoustic conditions – A new perspective on feature selection,” in Proc. of INTERSPEECH 2013, Lyon, France, pp. 2044-2048
F. Eyben, F. Weninger, S. Squartini, B. Schuller, “Real-life voice activity detection with LSTM Recurrent Neural Networks and an application to Hollywood movies,” in Proc. of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 483-487, 26-31 May 2013. doi: 10.1109/ICASSP.2013.6637694