1. l1-norm sparse Bayesian Learning
There are many ways to derive sparse solutions, like l1-norm regularization. But what is the optimal sparseness? You may do cross-validation, but is it the best way? My work of sparse Bayesian learning is to define optimal sparseness in a Bayesian framework and shows how to infer it from data. From more details, please see my thesis .
2. Speech dereverberation [ paper (NIPS 2007), slides ]
Sound examples for Fig. 3 in the paper. We carried out 10 experiments in total in a reverberant room. In each experiment, a sentence of
speech (approximately 1.5 seconds, and the same for all experiments) was played through a loud speaker (NSW2-326-8A, Aura Sound) and recorded by a matched omnidirectional microphone pair
(M30MP, Earthworks). The speaker-microphone positions (and thus RIRs) were different in different experiments.
Anechoic chamber measurement:
*Left: left microphone signal; Right: right microphone signal; Estimated: the estimated source by our method, blind sparse channel identification.
3. Time-difference-of-arrival (TDOA) estimation [ paper (WASPAA 2007), slides, video 1, video 2, video 3]
Videos 1~3 show how well our method, blind sparse-nonnegative (BSN) channel identification, tracks a moving loud speaker. The performance of phase transform (PHAT) is also presented for comparison.