Shahin Amiriparian received his Doctoral degree with highest honours (summa cum laude) at the Technical University of Munich, Germany in 2019. Currently, he is a postdoctoral researcher at the Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany. His main research focus is deep learning, unsupervised representation learning, and transfer learning for machine perception, affective computing, and audio understanding. He was involved in the ERC Starting Grant iHEARu and Horizon 2020 project DE-ENIGMA. He (co-)authored one book and more than 40 publications in peer-reviewed books, journals, and conference proceedings.

• S. Amiriparian, A. Bühlmeier, C. Henkelmann, M. Schmitt, B. Schuller, and O. Zeigermann, “Einstieg ins Machine Learning: Grundlagen, Prinzipien, erste Schritte,“ entwickler.press shortcuts, entwickler.press Software & Support Media GmbH, May 2019. 70 pages. Book available on Amazon.  Book available on Amazon.

• S. Amiriparian, J. Han, M. Schmitt, A. Baird, A. Mallol-Ragolta, M. Milling, M. Gerczuk, and B. Schuller, “Synchronisation in interpersonal speech,” Frontiers in Robotics and AI, vol. 6, p. 116, 2019.

• Won the best challenge paper award
  S. Amiriparian, M. Gerczuk, E. Coutinho, A. Baird, S. Ottl, M. Milling, and B. Schuller, “Emotion and Themes Recognition in Music Utilising Convolutional and Recurrent Neural Networks,” in MediaEval 2019, Sophia Antipolis, France, October 2019.

• S. Amiriparian, A. Awad, M. Gerczuk, L. Stappen, A. Baird, S. Ottl, and B. Schuller, “Audio-based Recognition of Bipolar Disorder Utilising Capsule Networks,” in Proceedings of 32nd International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, pp. 1-7, INNS/IEEE, IEEE, July 2019. 
• S. Amiriparian, M. Schmitt, S. Ottl, M. Gerczuk, and B. Schuller, “Deep Unsupervised Representation Learning for Audio-based Medical Applications,” in Deep Learners and Deep Learner Descriptors for Medical Applications , (L. Nanni, S. Brahnam, S. Ghidoni, R. Brattin, and L. Jain, eds.), Intelligent Systems Reference Library (ISRL), Springer, 2019. 27 pages.
• S. Amiriparian, N. Cummins, M. Gerczuk, S. Pugachevskiy, S. Ottl, and B. Schuller, “Are you playing a shooter again?!” deep representation learning for audio-based video game genre recognition,” IEEE Transactions on Games , 2019, 11 pages.
• S. Amiriparian, A. Baird, S. Julka, A. Alcorn, S. Ottl, S. Petrović, E. Ainger, N. Cummins, and B. Schuller, “Recognition of Echolalic Autistic Child Vocalisations Utilising Convolutional Recurrent Neural Networks,” in Proceedings of INTERSPEECH 2018, 19th Annual Conference of the International Speech Communication Association. Hyderabad, India:ISCA, September 2018, pp. 2334-2338.
• S. Amiriparian, M. Freitag, N. Cummins, M. Gerzcuk, S. Pugachevskiy, and B. Schuller, “A Fusion of Deep Convolutional Generative Adversarial Networks and Sequence to Sequence Autoencoders for Acoustic Scene Classification,” in Proceedings of the 26th European Signal Processing Conference (EUSIPCO), EURASIP. Rome, Italy: IEEE, September 2018, pp. 982-986.
• S. Amiriparian, M. Schmitt, N. Cummins, K. Qian, F. Dong, and B. Schuller, “Deep Unsupervised Representation Learning for Abnormal Heart Sound Classification,” in Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2018, IEEE. Honolulu, HI: IEEE, July 2018, pp. 4776-4779.
• S. Amiriparian, M. Gerczuk, S. Ottl, N. Cummins, S. Pugachevskiy, and B. Schuller, “Bag-of-deep-features: Noise-robust deep feature representations for audio analysis,” in Proceedings of the 31st International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil: IEEE, July 2018, pp. 2419-2425.
• S. Amiriparian, M. Schmitt, S. Hantke, V. Pandit, and B. Schuller, “Humans Inside: Cooperative Big Multimedia Data Mining,” in Innovations in Big Data Mining and Embedded Knowledge: Domestic and Social Context Challenges. ser. Intelligent Systems Reference Library (ISRL), A. Esposito, A. M. Esposito, and L. C. Jain, Eds. Springer, 2018, 25 pages.
• S. Amiriparian, S. Julka, N. Cummins, and B. Schuller, “Deep Convolutional Recurrent Neural Networks for Rare Sound Event Detection,” in Proceedings of 44. Jahrestagung für Akustik (DAGA), Munich, Germany, March 2018, pp. 1522-1525.
• S. Amiriparian, M. Freitag, N. Cummins, and B. Schuller, “Sequence To Sequence Autoencoders for Unsupervised Representation Learning From Audio,” in Proceedings of the 2nd Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE 2017). Munich, Germany: IEEE, November 2017, pp. 17-21.
• S. Amiriparian, N. Cummins, S. Ottl, M. Gerczuk, and B. Schuller, “Sentiment Analysis Using Image-based Deep Spectrum Features,” in Proceedings of the Biannual Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, TX, 2017, pp. 26-29.
• S. Amiriparian, M. Freitag, N. Cummins, and B. Schuller, “Feature Selection in Multimodal Continuous Emotion Prediction,” in Proceedings of the 2nd International Workshop on Automatic Sentiment Analysis in the Wild (WASA 2017) held in conjunction with the 7th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2017), AAAC. San Antonio, TX: IEEE, October 2017, pp. 30-37.
• S. Amiriparian, S. Pugachevskiy, N. Cummins, S. Hantke, J. Pohjalainen, G. Keren, and B. Schuller, “CAST a database: Rapid targeted large-scale big data acquisition via small-world modelling of social media platforms,” in Proceedings of the Biannual Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, TX, 2017, pp. 340-345.
• S. Amiriparian, M. Gerczuk, S. Ottl, N. Cummins, M. Freitag, S. Pugachevskiy, and B. Schuller, “Snore sound classification using image-based deep spectrum features,” in Proceedings of INTERSPEECH 2017, 18th Annual Conference of the International Speech Communication Association. Stockholm, Sweden: ISCA, August 2017. pp. 3512-3516.
• Nominated for best student paper award
  S. Amiriparian, J. Pohjalainen, E. Marchi, S. Pugachevskiy, and B. Schuller, “Is deception emotional? An emotion-driven predictive approach,” in Proceedings of INTERSPEECH 2016, 17th Annual Conference of the International Speech Communication Association. San Francisco, CA: ISCA, September 2016, pp. 2011-2015.
• S. Amiriparian, N. Cummins, M. Freitag, K. Qian, R. Zhao, V. Pandit and B. Schuller, “The Combined Augsburg / Passau / TUM / ICL System for DCASE 2017,” in Proceedings of the 2nd Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE 2017). Munich, Germany: IEEE, November 2017. 1 page. Technical report.
• M. Freitag, S. Amiriparian, S. Pugachevskiy, N. Cummins, and B. Schuller, “auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks,” Journal of Machine Learning Research, vol. 18, no. 173, pp. 1-5, 2018.
• A. Baird, S. Amiriparian, and B. Schuller, “Can Deep Generative Audio be Emotional? Towards an Approach for Personalised Emotional Audio Generation,” in Proceedings IEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019, (Kuala Lumpur, Malaysia), IEEE, IEEE, September 2019. 5 pages, to appear.
• A. Baird, S. Amiriparian, M. Berschneider, M. Schmitt, and B. Schuller, “Predicting Blood Volume Pulse and Skin Conductance from Speech: Introducing a Novel Database and Results,” in Proceedings IEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019, (Kuala Lumpur, Malaysia), IEEE, IEEE, September 2019. 5 pages, to appear
• N. Cummins, S. Amiriparian, S. Ottl, M. Gerczuk, M. Schmitt, and B. Schuller, “Multimodal Bag-of-Words for Cross Domains Sentiment Analysis,” in Proceedings of the 43rd IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, IEEE, April 2018. pp. 1-5.
• N. Cummins, S. Amiriparian, G. Hagerer, A. Batliner, S. Steidl, and B. Schuller, “An image-based deep spectrum feature representation for the recognition of emotional speech,”in Proceedings of the 25th ACM International Conference on Multimedia, MM 2017. Mountain View, CA: ACM, October 2017, pp. 478-484.
• B. W. Schuller, A. Batliner, C. Bergler, F. Pokorny, J. Krajewski, M. Cychosz, R. Vollmann, S.-D. Roelen, S. Schnieder, E. Bergelson, A. Cristia, A. Seidl, L. Yankowitz, E. Nöth, S. Amiriparian, S. Hantke, and M. Schmitt, “The INTERSPEECH 2019 Computational Paralinguistics Challenge: Styrian Dialects, Continuous Sleepiness, Baby Sounds & Orca Activity,” in Proceedings of INTERSPEECH 2019, 20th Annual Conference of the International Speech Communication Association, Graz, Austria, ISCA, ISCA, September 2019. 5 pages.
• B. Schuller, S. Steidl, A. Batliner, P. B. Marschik, H. Baumeister, F. Dong, S. Hantke, F. Pokorny, E.-M. Rathner, K. D. Bartl-Pokorny, C. Einspieler, D. Zhang, A. Baird, S. Amiriparian, K. Qian, Z. Ren, M. Schmitt, P. Tzirakis, and S. Zafeiriou, “The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats,” in Proceedings of INTERSPEECH 2018, 19th Annual Conference of the International Speech Communication Association. Hyderabad, India: ISCA, September 2018, pp. 122-126.
• M. Freitag, S. Amiriparian, N. Cummins, M. Gerczuk, and B. Schuller, “An ‘End-to-Evolution’ Hybrid Approach for Snore Sound Classification,” in Proceedings of INTERSPEECH 2017, 18th Annual Conference of the International Speech Communication Association. Stockholm, Sweden: ISCA, August 2017, pp. 3507-3511.
• V. Pandit, S. Amiriparian, M. Schmitt, A. Mousa, and B. Schuller, “Big Data Multimedia Mining: Feature Extraction facing Volume, Velocity, and Variety,” in Big Data Analytics for Large-Scale Multimedia Search, S. Vrochidis, B. Huet, E. Chang, and I. Kompatsiaris, Eds. Wiley, 2017.
• A. Baird, S. Amiriparian, N. Cummins, A. M. Alcorn, A. Batliner, S. Pugachevskiy, M. Freitag, M. Gerczuk, and B. Schuller, “Automatic Classification of Autistic Child Vocalisations: A Novel Database and Results,” in Proceedings of INTERSPEECH 2017, 18th Annual Conference of the International Speech Communication Association. Stockholm, Sweden: ISCA, Augsust 2017, pp. 849-853.
• A. Baird, S. Amiriparian, A. Rynkiewicz, and B. Schuller, “Echolalic Autism Spectrum Condition Vocalisations: Brute-Force and Deep Spectrum Features,” in Proceedings of the International Paediatric Conference (IPC 2018). Rzeszów, Poland: Polish Society of Social Medicine and Public Health, May 2018, 2 pages.
• F. Ringeval, S. Amiriparian, F. Eyben, K. Scherer, and B. Schuller, “Emotion Recognition in the Wild: Incorporating Voice and Lip Activity in Multimodal Decision-Level Fusion,” in Proceedings of the ICMI 2014 EmotiW - Emotion Recognition In The Wild Challenge and Workshop (EmotiW 2014), Satellite of the 16th ACM International Conference on Multimodal Interaction (ICMI 2014), Istanbul, Turkey, ACM, November 2014, pp. 473-480.
• F. Ringeval, B. Schuller, M. Valstar, R. Cowie, H. Kaya, M. Schmitt, S. Amiriparian, N. Cummins, D. Lalanne, A. Michaud, E. Ciftci, H. Gulec, A. A. Salah, and M. Pantic, “AVEC 2018 Workshop and Challenge: Bipolar Disorder and Cross-Cultural Affect Recognition,” in Proceedings of the 2018 on Audio/Visual Emotion Challenge and Workshop, ser. AVEC’18. Seoul, Republic of Korea: ACM, 2018, pp. 3-13.
• N. Cummins, M. Schmitt, S. Amiriparian, J. Krajewski, and B. Schuller, “You sound ill, take the day off: Classification of speech affected by Upper Respiratory Tract Infection,” in Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2017, Jeju Island, South Korea, IEEE, July 2017, pp. 3806-3809.
• F. Demir, A. Sengur, N. Cummins, S. Amiriparian, and B. Schuller, “Low level texture features for snore sound discrimination,” in Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2018, Honolulu, HI, IEEE, July 2018. pp. 413-416.
• A. Sengur, F. Demir, H. Lu, S. Amiriparian, N. Cummins, and B. Schuller, “Compact bilinear deep features for environmental sound recognition,” in Proceedings of the International Conference on Artificial Intelligence and Data Mining, IDAP 2018, Malatya, Turkey, IEEE, September 2018. 5 pages.
• S. Amiriparian, M. Schmitt, B. Schuller: “Exploiting Deep Learning: die wichtigsten Bits und Pieces - Sieh zu und lerne”, Java Magazin, Ausgabe 5.2018, S. 46-53, JAXenter, 2018.
• S. Amiriparian, A. Baird, B. Schuller, “Automatische Erkennung von Echolalie bei Kindern mit Autismus-Spektrum-Störung mithilfe direkter Mensch-Roboter-Interaktion”, Deutscher Kongress für Psychosomatische Medizin und Psychotherapie, Berlin, Deutschland, März 2019.
• M. Schmitt, S. Amiriparian, B. Schuller: “Maschinelle Sprachverarbeitung: Wie lernen Computer unsere Sprache zu verstehen?”, Entwickler Magazin, Spezial Volume 17, S. 30-38, Software & Support Media GmbH, 2018.

• Machine Learning, Deep Learning, Neural Networks, End-to-End Learning, mHealth, Affective Computing, Emotion Recognition, Human-Robot Interaction.

• Neural Network Development: Tensorflow, Caffe, Keras, Theano.
• Neural Network Approaches: Sequence to Sequence Autoencoders, Conditional Variational Autoencoders, Adaptive Neural Networks, End-to-End Learning, Reinforcement Learning, Zero-Shot Learning, (B)LSTMs and (B)GRUs, Convolutional Recurrent Neural Networks, (DC)GANs, CNNs, Pre-trained CNNs, MLPs.
• Neural Network Applications: Audio-based recognition tasks for Speech Emotion, Speaker Traits and States, Deception, Autism, Depression, Stroke, Acoustic Events, Keyword Spotting, Music Emotion.

• IEEE Transactions on Cybernetics. (IF: 8.803, 2018)
• IEEE Transactions on Neural Networks and Learning Systems. (IF: 7.982, 2018)
• IEEE Transactions on Biomedical Engineering (IF: 4.288, 2018)
• IEEE Transactions on Affective Computing. (IF: 4.585, 2018)
• IEEE Transactions on Computational Games.
• A range of conferences.

• Winner of the Science Slam Challenge 2018 in Augsburg, Germany. Topic: “Don’t be afraid of artificial intelligence”.
• Leistungsprämie für TV-L-Beschäftigte wegen ausgezeichneter Leistungen Performance bonus for TV-L employees due to excellent performances. University of Passau, faculty of Informatics and Mathematics.
• DAAD scholarship from STIBET III founding - Matching Funds. Technische Universität München and MicroNova AG.
• Deutschlandstipendium for outstanding performace in study.
• DAAD scholarship from STIBET program.

auDeep  

A Python toolkit for unsupervised feature learning with deep neural networks (DNNs).

Developers: Shahin Amiriparian, Michael Freitag, Sergey Pugachevskiy, Björn W. Schuller

GitHub: https://github.com/auDeep/auDeep

auDeep is a Python toolkit for unsupervised feature learning with deep neural networks (DNNs). Currently, the main focus of this project is feature extraction from audio data with deep recurrent autoencoders. However, the core feature learning algorithms are not limited to audio data. Furthermore, we plan on implementing additional DNN-based feature learning approaches.

(c) 2017 Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Björn Schuller: Universität Passau Published under GPLv3, see the LICENSE.md file for details.

Please direct any questions or requests to Shahin Amiriparian (shahin.amiriparian at tum.de) or Michael Freitag (freitagm at fim.uni-passau.de).

Citing

If you use auDeep or any code from auDeep in your research work, you are kindly asked to acknowledge the use of auDeep in your publications.

M. Freitag, S. Amiriparian, S. Pugachevskiy, N. Cummins, and B.Schuller. auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks, Journal of Machine Learning Research, 2017, submitted, 5 pages.

S. Amiriparian, M. Freitag, N. Cummins, and B. Schuller. Sequence to sequence autoencoders for unsupervised representation learning from audio, Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop, pp. 17-21, 2017.

DeepSpectrum

a Python toolkit for feature extraction from audio data with pre-trained Image Convolutional Neural Networks (CNNs).

Developers: Shahin Amiriparian, Maurice Gerczuk, Sandra Ottl, Björn W. Schuller

GitHub: https://github.com/DeepSpectrum/DeepSpectrum

DeepSpectrum is a Python toolkit for feature extraction from audio data with pre-trained Image Convolutional Neural Networks (CNNs). It features an extraction pipeline which first creates visual representations for audio data - plots of spectrograms or chromagrams - and then feeds them to a pre-trained Image CNN. Activations of a specific layer then form the final feature vectors.

(c) 2017-2018 Shahin Amiriparian, Maurice Gercuk, Sandra Ottl, Björn Schuller: Universität Augsburg Published under GPLv3, see the LICENSE.md file for details.

Please direct any questions or requests to Shahin Amiriparian (shahin.amiriparian at tum.de) or Maurice Gercuk (gerczuk at fim.uni-passau.de).

Citing
If you use DeepSpectrum or any code from DeepSpectrum in your research work, you are kindly asked to acknowledge the use of DeepSpectrum in your publications.

S. Amiriparian, M. Gerczuk, S. Ottl, N. Cummins, M. Freitag, S. Pugachevskiy, A. Baird and B. Schuller. Snore Sound Classification using Image-Based Deep Spectrum Features. In Proceedings of INTERSPEECH (Vol. 17, pp. 2017-434).