In connection with Sam Karimian-Azari’s Ph.D. defense, the two distinguished external assessment committee members will each give a talk on Wednesday September 28. The talks will take place in room 4.513 at 10:00-12:00.
Title: Plenacoustic Processing and the Ray Space Transform
Speaker: Prof. Augusto Sarti
Abstract: The literature of acoustic signal processing tends to rely on divide-and-conquer strategies derived from Fourier Acoustics, therefore it tends to inherit the limits that such a representation entails, in terms of resolution, frequency and far-field operation. Are there viable alternatives to this choice? In this talk I will first discuss what we can do with Plane-Wave Decomposition (PWD) and the related ray-based representation. I will then introduce the ray space and show how this can help overcoming the inherent limitations of such signal decomposition/processing tools. We will see, however, that more advantages come with rethinking our analysis approach and, in particular, our signal decomposition strategy. This we do by introducing a novel wave-field decomposition methodology based on Gabor frames, which is more suitable for local (in the space-time domain) representations. Based on this new framework for computational acoustics, I will introduce the ray-space transform and show how it can be used for efficiently and effectively approaching a far wider range of problems, ranging from simple source separation; to environment shape inference; to swift object-based manipulation of acoustic wavefields.
Title: Audio source separation: Challenges and recent advances
Speaker: Prof. Roland Badeau
Abstract: The classical problem of blind source separation (BSS) consists in recovering a number of unknown “source” signals from the observation of several “mixture” signals, by only assuming that the source signals are mutually independent. Independent component analysis (ICA) is a classical approach for solving this problem, when the mixture is linear instantaneous and (over-)determined. However, in the field of audio source separation, several challenging issues remain: for instance, the mixture is convolutive because of reverberation, and it is often under-determined and time-varying; source signals are non-stationary, and they often overlap in the time-frequency domain. Therefore audio source separation cannot be performed without exploiting some a priori knowledge about the source signals and about the mixture. In this talk, I will present some past and current investigations carried out at Telecom ParisTech to address these issues. Various parametric and probabilistic models will be introduced, that permit to exploit the information available about the source signals and about the mixtures. An application to score-based separation of musical sources will be presented.