Nilesh Madhu
Nilesh Madhu
tel.: +32 488 04 25 14
research unit: Internet Technology and Data Science Lab (IDLab)
website
Nilesh Madhu received his Dr.-Ing. degree (summa cum laude) from the Ruhr-Universität Bochum, Germany, in 2009. His dissertation was on signal processing algorithms for the localisation and separation of acoustic sources using microphone arrays. Following this he was awarded a Marie-Curie experienced researcher fellowship for a two-year postdoctoral stay at the KU Leuven, Belgium. Here he successfully applied his signal processing knowledge to the field of hearing prostheses and biomedical signal analysis.
From 2011 to 2017 he was with NXP Semiconductors, Belgium, where he held the positions of principal scientist and team lead within the product line Mobile Audio Solutions. During this period he and his team were tasked with developing innovative, beyond the state-of-the-art algorithms for audio and speech enhancement in mobile communications devices. This work, consistently held to exacting industry standards and strict delivery deadlines, led to the successful deployment of the algorithms on several flagship models of major smartphone OEM’s.
Since December 2017 he is a professor for audio and speech processing at Ghent University and imec, Belgium. He is passionate about signal processing and is especially interested in machine learning approaches for signal detection and enhancement for various applications in the fields of communications, healthcare, education and automation.
Keywords: Machine learning, Audio & speech enhancement, Automated audio scene analysis and tagging, Automatic audio quality evaluation, Hearing aids and cochlear implants
- S. Elshamy, N. Madhu, W. Tirry and T. Fingscheidt, “DNN-Supported Speech Enhancement With Cepstral Estimation of Both Excitation and Envelope,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 12, pp. 2460-2474, Dec. 2018.
- S. Elshamy, N. Madhu, W. Tirry, and T. Fingscheidt, “A priori SNR computation for speech enhancement based on cepstral envelope estimation,” in Proceedings of the 16th International Workshop on Acoustic Signal Enhancement (IWAENC), pp. 351–355, Sept. 17-20, 2018.
- S. Gergen, R. Martin, and N. Madhu, “Source separation by feature-based clustering of microphones in ad hoc arrays,” in Proceedings of the 16th International Workshop on Acoustic Signal Enhancement (IWAENC), pp. 1–5, 2018, Sept. 17-20, 2018.
- S. Kitic, L. Jacques, N. Madhu, M. P. Hopwood, A. Spriet and C. De Vleeschouwer, “Consistent iterative hard thresholding for signal declipping,” 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, 2013, pp. 5939-5943.
- N. Madhu and R. Martin, “A Versatile Framework for Speaker Separation Using a Model-Based Speaker Localization Approach,” in IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 7, pp. 1900-1912, Sept. 2011.