activity
20162023
most citedFaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing

30 citations · 90 across the 19 of their papers we have counts for

collaborators
Showing 2020Show all

11 papers · 1 filter

eess.AS2020★ 6 cited

Continuous Speech Separation Using Speaker Inventory for Long Multi-talker Recording

Cong Han, Yi Luo, Chenda Li +8

Leveraging additional speaker information to facilitate speech separation has received increasing attention in recent years. Recent research includes extracting target speech by us…

eess.AS2020

Group Communication with Context Codec for Lightweight Source Separation

Yi Luo, Cong Han, Nima Mesgarani

Despite the recent progress on neural network architectures for speech separation, the balance between the model size, model complexity and model performance is still an important…

eess.AS2020★ 3 cited

Ultra-Lightweight Speech Separation via Group Communication

Yi Luo, Cong Han, Nima Mesgarani

Model size and complexity remain the biggest challenges in the deployment of speech enhancement and separation systems on low-resource devices such as earphones and hearing aids. A…

eess.AS2020★ 1 cited

Implicit Filter-and-sum Network for Multi-channel Speech Separation

Yi Luo, Nima Mesgarani

Various neural network architectures have been proposed in recent years for the task of multi-channel speech separation. Among them, the filter-and-sum network (FaSNet) performs en…

eess.AS2020★ 3 cited

Rethinking the Separation Layers in Speech Separation Networks

Yi Luo, Zhuo Chen, Cong Han +3

Modules in all existing speech separation networks can be categorized into single-input-multi-output (SIMO) modules and single-input-single-output (SISO) modules. SIMO modules gene…

eess.AS2020★ 1 cited

Distortion-controlled Training for End-to-end Reverberant Speech Separation with Auxiliary Autoencoding Loss

Yi Luo, Cong Han, Nima Mesgarani

The performance of speech enhancement and separation systems in anechoic environments has been significantly advanced with the recent progress in end-to-end neural network architec…