Roland Badeau is Full Professor in the Signal, Statistics and Machine learning (S2A) team of the Image, Data, Signal (IDS) Department at Télécom Paris. His research interests focus on statistical modeling of non-stationary signals (including adaptive high-resolution spectral analysis and Bayesian extensions to NMF), with applications to audio and music (source separation, denoising, dereverberation, multipitch estimation, automatic music transcription, audio coding, audio inpainting). He is a co-author of over 30 journal papers, over 130 international conference papers, a book chapter and 4 patents.

• Room acoustics: statistical modeling of reverberation
• Data representation: dimensionality reduction, time-frequency analysis (high resolution)
• Modeling: probabilistic latent variable models, source models (positive matrix factorizations, sinusoidal models, sparse models, etc.), propagation models (convolutional, diffuse)
• Algorithms: Bayesian estimation, optimization methods, fast adaptive algorithms, performance analysis, convergence speed, numerical stability, algorithmic complexity
• Applications to audio signals: source separation/localization, audio coding, restoration, denoising, dereverberation, sound scene analysis, music information retrieval
• Other applications: biomedical data analysis, digital communications, image processing