22 citations · 22 across the 14 of their papers we have counts for
14 papers
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…
GAN-Based Multi-Microphone Spatial Target Speaker Extraction
Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
Spatial target speaker extraction isolates a desired speaker's voice in multi-speaker environments using spatial information, such as the direction of arrival (DoA). Although recen…
Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement
Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
Generative speech enhancement methods based on generative adversarial networks (GANs) and diffusion models have shown promising results in various speech enhancement tasks. However…
Low-Complexity Neural Wind Noise Reduction for Audio Recordings
Hesam Eftekhari, Srikanth Raj Chetupalli, Shrishti Saha Shetu +2
Wind noise significantly degrades the quality of outdoor audio recordings, yet remains difficult to suppress in real-time on resource-constrained devices. In this work, we propose…
GAN-Based Speech Enhancement for Low SNR Using Latent Feature Conditioning
Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
Enhancing speech quality under adverse SNR conditions remains a significant challenge for discriminative deep neural network (DNN)-based approaches. In this work, we propose DisCoG…
Align-ULCNet: Towards Low-Complexity and Robust Acoustic Echo and Noise Reduction
Shrishti Saha Shetu, Naveen Kumar Desiraju, Wolfgang Mack +1
The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, w…
A Hybrid Approach for Low-Complexity Joint Acoustic Echo and Noise Reduction
Shrishti Saha Shetu, Naveen Kumar Desiraju, Jose Miguel Martinez Aponte +2
Deep learning-based methods that jointly perform the task of acoustic echo and noise reduction (AENR) often require high memory and computational resources, making them unsuitable…
Comparative Analysis Of Discriminative Deep Learning-Based Noise Reduction Methods In Low SNR Scenarios
Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
In this study, we conduct a comparative analysis of deep learning-based noise reduction methods in low signal-to-noise ratio (SNR) scenarios. Our investigation primarily focuses on…
Ultra Low Complexity Deep Learning Based Noise Suppression
Shrishti Saha Shetu, Soumitro Chakrabarty, Oliver Thiergart +1
This paper introduces an innovative method for reducing the computational complexity of deep neural networks in real-time speech enhancement on resource-constrained devices. The pr…
An Empirical Study of Visual Features for DNN based Audio-Visual Speech Enhancement in Multi-talker Environments
Shrishti Saha Shetu, Soumitro Chakrabarty, Emanuël A. P. Habets
Audio-visual speech enhancement (AVSE) methods use both audio and visual features for the task of speech enhancement and the use of visual features has been shown to be particularl…