Axel Roebel

Biography:
<h3 id="Bio">Biographie</h3> <p>Axel Roebel is research director at IRCAM and head of the <a href="/recherche/equipes-recherche/anasyn/">Sound Analysis-Synthesis team</a> (AS). He received the Diploma in electrical engineering from Hannover University in 1990 and the Ph.D. degree (summa cum laude) in computer science from the Technical University of Berlin in 1993. In 1994 he joined the German National Research Center for Information Technology (GMD-First) in Berlin where he continued his research on using articial neural networks for modeling of time series of nonlinear dynamical systems. In 1996 he became assistant professor for digital signal processing in the communication science department of the Technical University of Berlin.</p> <p>In 2000, he obtained a research scholarship at CCRMA, Standford University, where he started an investigation into adaptive sinusoidal modeling. In 2000 he joined the <a href="/recherche/equipes-recherche/anasyn/">Sound Analysis-Synthesis team</a> of IRCAM where he obtained his Habilitation from the Sorbonne Universit&eacute; in 2011 and where became research director in 2013. He has developed state of the art speech and music analysis and transformation algorithms, is the author numerous libraries for signal analysis, synthesis and transformation as for example SuperVP, a software for music and speech signal analysis and transformation that has been integrated in numerous professional audio tools. He has recently started to investigate signal processing algorithms based on deep learning. He has published more than 100 publications in international journals and conferences.</p> <h4>Research topics</h4> <h5>Voice processing</h5> <ul> <li>speech analysis (F0, voiced/unvoiced, glottal source)</li> <li>singing synthesis</li> <li>speech transformation - (shape invariant phase vocoder, extended source-filter speech models (PaN), neural vocoder)</li> <li>singing voice separation</li> <li>deep learning based speech analysis, processsing and transformation.</li> </ul> <h5>Music</h5> <ul> <li>high quality signal transformation based on the phase vocoder representation</li> <li>additive signal models using advanced algorithms for the analysis and representation of non-stationary signals and the development of Pm2, IRCAMs software for sinusoidal analysis/synthesis.</li> <li>structured signal models and perceptually pertinent signal descriptors (fundamental frequency, spectral envelope, ...)</li> <li>signal decomposition</li> <li>polyphonic f0 estimation</li> </ul> <h4>Development activities</h4> <ul> <li>ISiS: singing synthesis software written in python.</li> <li>as_pysrc: python packages for signal processing</li> <li>Deep learning based signal processing in Tensorflow</li> <li>SuperVP: an extended phase vocoder software allowing high quality transformations of music and speech signals, implementing new techniques for spectral envelope estimation and transformation. SuperVP is a cross platform library that is used in from of a command line application (SuperVP) that is used in AudioSculpt and OpenMusic as well as in form of a real time signal transformation modules which is used in Max/MSP and SuperVP-TRaX.</li> <li>VoiceForger: real time voice transformation library based on SuperVP.</li> <li>Pm2 library and application for analysis/synthesis using advanced sinusoidal signal models</li> <li>MatMTL a matlab compatible c++ template library</li> <li>LibFFT a support library for cross platform vectorized FFT calculation</li> </ul> <p></p>

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