22 citations · 22 across the 14 of their papers we have counts for
5 papers · 1 filter
Discriminative Flow Matching: Beyond Time-Conditioning in Generative Restoration via Flow-State Representations
Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
Existing Conditional Flow Matching (CFM) formulations describe transport progress using an explicit interpolation coordinate, commonly interpreted as time, assuming that a single g…
GAN-based Joint Dereverberation and Directional Filtering
Weilong Huang, Shrishti Saha Shetu, Emanuël A. P. Habets
Recently, neural directional filtering (NDF) enables reconstruction of a virtual directional microphone (VDM) with a desired directivity pattern, accurately rendering multi-source…
SlimDiffuSE: Towards Efficient Diffusion-Based Speech Enhancement using Slimmable Networks
Nagashree K. S. Rao, Shrishti Saha Shetu, Mohamed Elminshawi +2
Diffusion-based models are emerging in the speech enhancement domain and are achieving state-of-the-art performance across various benchmark datasets. A major downside of diffusion…
μNet: Ultra-Low-Memory and Low-Complexity Speech Enhancement for Embedded Digital Signal Processors
Shrishti Saha Shetu, Jose Miguel Martinez Aponte, Nagashree K. S. Rao +3
Speech enhancement on embedded digital signal processors (DSPs) imposes strict constraints on memory footprint, computational complexity, latency, and support for integer operation…
A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination
Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
In this study, we conduct a comprehensive comparative analysis of generative and discriminative deep learning-based speech enhancement methods, specifically in noise reduction task…