7 papers
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…