12 citations · 13 across the 15 of their papers we have counts for
16 papers
Speaker Tracking: Segment-online Multi-talker Organization with a Varying Number of Speakers
Vahid A. Kalkhorani, Daniel Wong, Jacob Donley +3
Speaker tracking is the task of separating and following multiple speakers over time. It must address overlapped speech, speech onset and offset, talker identity, and time-varying…
Cloud-Boosted Low-Compute Multi-Channel Speech Enhancement
Xulin Fan, Juan Azcarreta, Ashutosh Pandey +5
Low-latency, low-compute speech enhancement is essential for wearable devices with real-time communication requirements, but strict computational constraints significantly limit on…
Spatial-Magnifier: Spatial upsampling for multichannel speech enhancement
Dongheon Lee, Ashutosh Pandey, Sanjeel Parekh +4
While the spatial directivity of multichannel speech enhancement algorithms improves with the number of microphones, fitting large capture arrays into real-world edge devices is ty…
Unified Diffusion Refinement for Multi-Channel Speech Enhancement and Separation
Zhongweiyang Xu, Ashutosh Pandey, Juan Azcarreta +4
We propose Uni-ArrayDPS, a novel diffusion-based refinement framework for unified multi-channel speech enhancement and separation. Existing methods for multi-channel speech enhance…
ArrayDPS-Refine: Generative Refinement of Discriminative Multi-Channel Speech Enhancement
Zhongweiyang Xu, Ashutosh Pandey, Juan Azcarreta +3
Multi-channel speech enhancement aims to recover clean speech from noisy multi-channel recordings. Most deep learning methods employ discriminative training, which can lead to non-…
Improving Resource-Efficient Speech Enhancement via Neural Differentiable DSP Vocoder Refinement
Heitor R. Guimarães, Ke Tan, Juan Azcarreta +4
Deploying speech enhancement (SE) systems in wearable devices, such as smart glasses, is challenging due to the limited computational resources on the device. Although deep learnin…
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…
A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control
Yuan-Kuei Wu, Juan Azcarreta, Kashyap Patel +4
This study presents a deep-learning framework for controlling multichannel acoustic feedback in audio devices. Traditional digital signal processing methods struggle with convergen…
Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancement
Longbiao Cheng, Ashutosh Pandey, Buye Xu +3
Deep learning-based speech enhancement (SE) methods often face significant computational challenges when needing to meet low-latency requirements because of the increased number of…
Dynamic Gated Recurrent Neural Network for Compute-efficient Speech Enhancement
Longbiao Cheng, Ashutosh Pandey, Buye Xu +2
This paper introduces a new Dynamic Gated Recurrent Neural Network (DG-RNN) for compute-efficient speech enhancement models running on resource-constrained hardware platforms. It l…
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…
All Neural Low-latency Directional Speech Extraction
Ashutosh Pandey, Sanha Lee, Juan Azcarreta +2
We introduce a novel all neural model for low-latency directional speech extraction. The model uses direction of arrival (DOA) embeddings from a predefined spatial grid, which are…
Speech enhancement deep-learning architecture for efficient edge processing
Monisankha Pal, Arvind Ramanathan, Ted Wada +1
Deep learning has become a de facto method of choice for speech enhancement tasks with significant improvements in speech quality. However, real-time processing with reduced size a…
On the Importance of Neural Wiener Filter for Resource Efficient Multichannel Speech Enhancement
Tsun-An Hsieh, Jacob Donley, Daniel Wong +2
We introduce a time-domain framework for efficient multichannel speech enhancement, emphasizing low latency and computational efficiency. This framework incorporates two compact de…
Decoupled Spatial and Temporal Processing for Resource Efficient Multichannel Speech Enhancement
Ashutosh Pandey, Buye Xu
We present a novel model designed for resource-efficient multichannel speech enhancement in the time domain, with a focus on low latency, lightweight, and low computational require…
Multichannel Speech Enhancement without Beamforming
Asutosh Pandey, Buye Xu, Anurag Kumar +3
Deep neural networks are often coupled with traditional spatial filters, such as MVDR beamformers for effectively exploiting spatial information. Even though single-stage end-to-en…