Nasser Mohammadiha

Adjunct docent, Data Science and AI division, Department of Computer Science and Engineering.

I am an Adjunct Docent in the Machine Learning group, Department of Computer Science and Engineering, and a technical specialist in Machine Learning at Zenuity, where I work with active safety systems and autonomous driving. My main research interests include machine learning for perception and planning and big data analysis.
Latest Publications:
2018
  •  J. Martinsson, N. Mohammadiha, A. Schliep, “Clustering Vehicle Motion Trajectories Using  Finite Mixtures of Hidden Markov Models”, International Conference on Intelligent Transportation Systems (ITSC), 2018. 
  • E. L. Zec, N. Mohammadiha, A. Schliep, “Modelling Autonomous Driving Sensors Using  Hidden Markov Models”, International Conference on Intelligent Transportation Systems (ITSC), 2018. 
  • E. Karlsson, N. Mohammadiha, “A Statistical GPS Error Model for Autonomous Driving”,  in IEEE Intelligent Vehciles Symposium (IV), 2018. 
  • E. Karlsson, N. Mohammadiha, “A Data-driven Generative Model for GPS Sensors for  Autonomous Driving”, Software Engineering for AI in Autonomous Systems (SEFAIAS),  2018.
2017
  • C. Innocenti, H. Lindén, G. Panahandeh, L. Svensson, and N. Mohammadiha, "Imitation Learning for Vision-based Lane Keeping Assistance ",  International Conference on Intelligent Transportation Systems (ITSC), 2017
  • C. Innocenti, H. Lindén, G. Panahandeh, L. Svensson, and N. Mohammadiha, "Imitation Learning for Autonomous Driving", The first Swedish Symposium on Deep Learning (SSDL), 2017.
  •  A. Bender, E. M. Thorsteinsson, P. Nordin, and N. Mohammadiha, " Object Classification using Convolutional Neural Networks with 3D Intensity Voxel Matrices", The first Swedish Symposium on Deep Learning (SSDL), 2017.
  • A. Tashvir, J. Sjöberg, N. Mohammadiha, "Sensor Error Prediction and Anomaly Detection Using Neural Networks", The first Swedish Symposium on Deep Learning (SSDL), 2017.
  • L. D. Solana, D. Scanlan, G. Panahandeh, and N. Mohammadiha, "Residual Connections in Light-Weight Convolutional Neural Network Object Detectors", The first Swedish Symposium on Deep Learning (SSDL), 2017.
  • N Mohammadiha, "A Short Review of Deep Learning Applications for Autonomous Driving", The first Swedish Symposium on Deep Learning (SSDL), 2017.
  • N. Mohammadiha, P. Nygren, M. Jasinski,  "A Comparison of Classification Methods for 3D Point Clouds", Fourth Internetional Symposium on Future Active Safety Technology Toward zero traffic accidents (FAST-zero), 2017.
  • G. Panahandeh, Erik Ek, N. Mohammadiha, "Road Friction Prediction using Supervised Machine Learning and Connected Vehicles," in IEEE Intelligent Vehciles Symposium (IV), 2017.

2016

  • N. Mohammadiha, S. Doclo, “Speech Dereverberation Using Non-negative Convolutive Transfer Function and Spectro-temporal Modeling”, IEEE Trans. Audio, Speech and Language Process., vol. 24, no. 2, pp. 276–289, Feb. 2016.
  • J. Florbäck, L. Tornberg, N. Mohammadiha, “Offline Object Matching and Evaluation Process for Verification of Autonomous Driving”, in Proc. IEEE Intelligent Transportation Systems Conference (ITSC), Rio de Janeiro, Nov. 2016.
2015
  • N. Mohammadiha, P. Smaragdis, S. Doclo, “Joint Acoustic And Spectral Modeling for Speech Dereverberation Using Non-Negative Representations,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Process. (ICASSP), Brisbane, Autralia, Apr. 2015. 
  • A. Asaei, N. Mohammadiha, M. J. Taghizadeh, S. Doclo H. Bourlard, “On Application of Non-Negative Matrix Factorization for Ad Hoc Microphone Array Calibration From Incomplete Noisy Distances,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Process. (ICASSP), Brisbane, Autralia, Apr. 2015.
  • S. Karimian-Azari, N. Mohammadiha, J. R. Jensen, and M. G. Christensen, “Pitch Estimation and Tracking with Harmonic Emphasis on the Acoustic Spectrum,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Process. (ICASSP), Brisbane, Autralia, Apr. 2015.
  • A. Juki´c, N. Mohammadiha, T. vanWaterschoot, T. Gerkmann, S. Doclo, “Multi-Channel Linear Prediction-Based Speech Dereverberation With Low-Rank Power Spectrogram Approximation ,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Process. (ICASSP), Brisbane, Autralia, Apr. 2015.
2014
  • N. Mohammadiha, P. Smaragdis, G. Panahandeh, S. Doclo, “A State-Space Approach to Dynamic Nonnegative Matrix Factorization”, IEEE Trans. Signal Process., vol. 63, no. 4, pp. 949–959, Dec. 2014.
  • P. Smaragdis, C. Févotte, G. J. Mysore, N. Mohammadiha, M. Hoffman , “A Unified View of Static and Dynamic Source Separation Using Non-Negative Factorizations”, IEEE Signal Process. Magazine, vol. 31, no. 3, pp. 66–75, May 2014.
  • N. Mohammadiha, S. Doclo, “Single-channel Dynamic Exemplar-based Speech Enhancement, in Proc. Interspeech, Sep., 2014.
  • N. Mohammadiha, S. Doclo, “Transient Noise Reduction Using Nonnegative Matrix Factorization,” in Proc. Hands-free Speech Communication and Microphone Arrays (HSCMA), May, 2014.
2013
  • N. Mohammadiha, “Speech Enhancement Using Nonnegative Matrix Factorization and Hidden Markov Models,” PhD Thesis, 2013.
  • N. Mohammadiha, P. Smaragdis, A. Leijon, “Supervised and Unsupervised Speech Enhancement Approaches using Nonnegative Matrix Factorization,” IEEE Trans. Audio, Speech and Language Process., vol. 21, no. 10, pp. 2140–2151, Oct. 2013.
  • N. Mohammadiha, A. Leijon, “NonnegativeHMMfor Babble Noise Derived from Speech HMM: Application to Speech Enhancement,” IEEE Trans. Audio, Speech and Language Process., vol. 21, no. 5, pp. 998–1011, May 2013.
  • N. Mohammadiha, R. Martin, A. Leijon, “Spectral Domain Speech Enhancement using HMM State-Dependent Super-Gaussian Priors,” IEEE Signal Processing Letters, vol. 20, no. 3, pp. 253–256, Mar. 2013.
  • G. Panahandeh, N. Mohammadiha, A. Leijon, P. Händel, “Continuous Hidden Markov Model for Pedestrian Activity Classification and Gait Analysis,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 5, pp. 1073–1083, May 2013.
  • N. Mohammadiha, P. Smaragdis, and A. Leijon, “Low-artifact Source Separation Using Probabilistic Latent Component Analysis,” in Proc. IEEE Workshop Applications of Signal Process. Audio Acoustics (WASPAA), oct. 2013.
  • N. Mohammadiha, P. Smaragdis, A. Leijon, “Simultaneous Noise Classification and Reduction Using a Priori Learned Models ,” IEEE Int. Workshop on Machine Learning for Signal Process. (MLSP), sep. 2013.
  • N. Mohammadiha, W. B. Kleijn, A. Leijon, “Gamma Hidden Markov Model as a Probabilistic Nonnegative Matrix Factorization ,” in Proc. European Signal Process. Conf. (EUSIPCO), sep. 2013, winner of best paper award.
  • N. Mohammadiha, P. Smaragdis, and A. Leijon, “Prediction Based Filtering and Smoothing to Exploit Temporal Dependencies in NMF,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Process. (ICASSP), may 2013, pp. 873–877.

Published: Tue 07 Mar 2017. Modified: Tue 15 Jan 2019