Alexandru Nelus M.Sc.

Ruhr-Universität Bochum
Institut für Kommunikationsakustik
Fakultät für Elektrotechnik und Informationstechnik
Universitätsstr. 150
D-44780 Bochum
Raum: ID/2/221

Email: alexandru.nelus@rub.de
Tel.: +49 234 32 25388

Publications

A. Nelus, J. Ebbers, R. Haeb-Umbach, and R. Martin, “Privacy-preserving variational information feature extraction for domestic activity monitoring versus speaker identification”. Accepted at INTERSPEECH, IEEE, 2019.

A. Nelus, S. Rech, T. Koppelmann, H. Biermann, and R. Martin. “Privacy-preserving siamese feature extraction for gender recognition versus speaker identification”. Accepted at INTERSPEECH, IEEE, 2019.

A. Nelus and R. Martin. “Privacy-aware feature extraction for gender discrimination versus speaker identification”. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019.

A. Nelus and R. Martin, “Gender discrimination versus speaker identification through privacy-aware adversarial feature extraction,” in Speech Communication; 13. ITG Symposium; Proceedings of, VDE, 2018.

J. Ebbers, A. Nelus, R. Martin, and R. Häb-Umbach, “Evaluation of modulation-mfcc features and dnn classification for acoustic event detection,” in Deutsche Jahrestagung fur Akustik (DAGA), 2018.

A. Nelus, S. Gergen, and R. Martin, “Analysis of temporal aggregation and dimensionality reduction on feature sets for speaker identification in wireless acoustic sensor networks,” in Multimedia Signal Processing (MMSP), 2017 IEEE 19th International Workshop on, pp. 1–6, IEEE, 2017.

A. Nelus, S. Gergen, J. Taghia, and R. Martin, “Towards opaque audio features for privacy in acoustic sensor networks,” in Speech Communication; 12. ITG Symposium; Proceedings of, pp. 1–5, VDE, 2016.

A. Nelus, M. Nicolae, and D. Popescu, “Simulation framework for wsn used in monitoring of illegal tree cutting,” University Politehnica of Bucharest Scientific Bulletin Series C-Electrical Engineering and Computer Science, vol. 78, no. 3, pp. 27–38, 2016.

Awards

Best Student Paper Award at 13th ITG Conference on Speech Communication Oldenburg, October 10-12, 2018. Paper: A. Nelus and R. Martin, “Gender discrimination versus speaker identification through privacy-aware adversarial feature extraction,”

Other

Presentation on “Privacy-preserving Adversarial Feature Extraction in Speaker Classification Tasks” at Data Science Ruhrgebiet 2019.

Organizer of Android @RUB Hackathon 2019, Institut für Kommunikationsakustik, Ruhr-Universität Bochum. https://www.eventbrite.com/e/android-rub-hackathon-2019-tickets-53256492603#

Poster presentation A. Nelus and R. Martin, “Scalable audio features for clustering and classification with privacy constraints,” in Satellite Workshop „Acoustic Sensor Networks“ in Speech Communication; 13. ITG Symposium; 2018.

Organizer of Android @RUB Hackathon 2017, Institut für Kommunikationsakustik, Ruhr-Universität Bochum. https://www.eventbrite.com/e/android-rub-hackathon-tickets-33657751240# 

Coordinator of AppTeam Ruhr University Bochum, https://play.google.com/store/apps/developer?id=AppTeam+Ruhr+University+Bochum.

Teaching

Bachelor-Vertiefungspraktikum Elektrotechnik und Informationstechnik IT-V3  WS15, WS16, WS17, WS18.

Bachelor-Vertiefungsseminar Informationstechnik SS16, SS17, SS18, SS19.

Grundlagen der Sprachsignalverarbeitung  WS16.

Bachelor project supervision:

  • Audio signal labeling and classification on Android based embedded devices.
  • Real-time localization, beamforming and noise reduction, joint supervision with Mehdi Zohourian.
  • Music genre classification using Mod-MFCC features.
  • Gender discrimination vs. speaker identification through privacy-aware siamese feature extraction.
  • Implementation of privacy-preserving feature extraction in a distributed acoustic sensor network.
  • End-to-end approximation of auditory models using artificial neuronal networks, joint supervision with Anil Nagathil.

Bachelor thesis supervision:

  • Audio feature extraction with privacy constraints on Android based embedded devices.
  • Audio signal classification with privacy constraints on Android based embedded devices.
  • Automatic classification of moving vehicles using audio signals, joint supervision with Johannes Gauer.
  • Generation of cryptographic keys using the available information of acoustic channels      .
  • Content- and context-based classification of music signals using deep neural networks.
  • Gender discrimination vs. speaker identification through privacy-aware siamese feature extraction.
  • Analysis of privacy-preserving feature extraction schemes for domestic activity monitoring vs. speaker identification.

Foto Alexandru Nelus