Axel Roebel

Biography:
<h3 id="Bio">Biography</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 artificial 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 he became Research Director in 2013. He has developed state-of-the-art speech and music analysis and transformation algorithms. He is the author of numerous libraries for signal analysis, synthesis, and transformation, for example, SuperVP, a software for music and speech signal analysis and transformation that has been integrated into numerous professional audio tools. His ongoing research focuses on advancing deep learning techniques for tasks related to music and voice processing. This notably includes neural vocoding, the exploration of signal representation and manipulation within latent spaces, as well as the investigation of disentangling strategies in these latent spaces.</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, processing, and transformation,</li> <li>neural vocoder.</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>Multi-band Excited WaveNet Neual Vocoder (<a href="https://github.com/roebel/MBExWN_Vocoder">MBExWN</a>)</li> <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, and implementing new techniques for spectral envelope estimation and transformation. SuperVP is a cross-platform library that is available in form of a command-line application (SuperVP), which is used in AudioSculpt and OpenMusic as well as in form of a real-time signal transformation module, 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>

App:

Model:

Id:

Fields: