3 accepted papers
In this paper, we propose to extend the deep, complex U-Network architecture for speech enhancement by incorporating a probabilistic (i.e., variational) latent space model. The proposed model is evaluated against several ablated versions of itself in order to study the effects of the variational lat…
Classic approaches to multi-channel signal enhancement rely on model assumptions regarding speech source relative transfer functions and noise covariance matrix, or on estimates thereof obtained in, e.g., speech pauses. To alleviate these constraints, we here investigate an approach to adaptive esti…
This contribution investigates the use of features derived from a Gabor filterbank (GFB) for the application of acoustic cough classification. Gabor filters are two-dimensional filters that decompose the spectro-temporal power density further into components which capture spectral, temporal and join…