6 citations · 6 across the 9 of their papers we have counts for
9 papers
Towards Localizing Conversation Partners using Head Motion
Payal Mohapatra, Calvin Murdock, Ali Aroudi +4
Many individuals struggle to understand conversation partners in noisy settings, particularly amid background speakers or due to hearing impairments. Emerging wearables like smartg…
Controlling the Parameterized Multi-channel Wiener Filter using a tiny neural network
Eric Grinstein, Ashutosh Pandey, Cole Li +6
Noise suppression and speech distortion are two important aspects to be balanced when designing multi-channel Speech Enhancement (SE) algorithms. Although neural network models hav…
Microphone Occlusion Mitigation for Own-Voice Enhancement in Head-Worn Microphone Arrays Using Switching-Adaptive Beamforming
Wiebke Middelberg, Jung-Suk Lee, Saeed Bagheri Sereshki +3
Enhancing the user's own-voice for head-worn microphone arrays is an important task in noisy environments to allow for easier speech communication and user-device interaction. Howe…
Non-verbal Hands-free Control for Smart Glasses using Teeth Clicks
Payal Mohapatra, Ali Aroudi, Anurag Kumar +1
Smart glasses are emerging as a popular wearable computing platform potentially revolutionizing the next generation of human-computer interaction. The widespread adoption of smart…
FoVNet: Configurable Field-of-View Speech Enhancement with Low Computation and Distortion for Smart Glasses
Zhongweiyang Xu, Ali Aroudi, Ke Tan +4
This paper presents a novel multi-channel speech enhancement approach, FoVNet, that enables highly efficient speech enhancement within a configurable field of view (FoV) of a smart…
TRUNet: Transformer-Recurrent-U Network for Multi-channel Reverberant Sound Source Separation
Ali Aroudi, Stefan Uhlich, Marc Ferras Font
In recent years, many deep learning techniques for single-channel sound source separation have been proposed using recurrent, convolutional and transformer networks. When multiple…
DBNET: DOA-driven beamforming network for end-to-end farfield sound source separation
Ali Aroudi, Sebastian Braun
Many deep learning techniques are available to perform source separation and reduce background noise. However, designing an end-to-end multi-channel source separation method using…
Cognitive-driven convolutional beamforming using EEG-based auditory attention decoding
Ali Aroudi, Marc Delcroix, Tomohiro Nakatani +3
The performance of speech enhancement algorithms in a multi-speaker scenario depends on correctly identifying the target speaker to be enhanced. Auditory attention decoding (AAD) m…
Improving auditory attention decoding performance of linear and non-linear methods using state-space model
Ali Aroudi, Tobias de Taillez, Simon Doclo
Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possibl…