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20192026
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eess.AS2026

COSED: Setting the Bar for Open-Vocabulary Sound Event Detection

Florian Schmid, Sanjeel Parekh, Chi Ian Tang +5

Open-vocabulary Sound Event Detection detects and temporally localizes acoustic events described by arbitrary text queries. Progress in this emerging field is hard to assess: recen…

eess.AS2026

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…

eess.AS2026

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…

eess.AS2026

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-…

eess.AS2025

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…

eess.AS2020

Improving Sound Event Detection Metrics: Insights from DCASE 2020

Giacomo Ferroni, Nicolas Turpault, Juan Azcarreta +4

The ranking of sound event detection (SED) systems may be biased by assumptions inherent to evaluation criteria and to the choice of an operating point. This paper compares convent…

eess.AS2019

A Framework for the Robust Evaluation of Sound Event Detection

Cagdas Bilen, Giacomo Ferroni, Francesco Tuveri +2

This work defines a new framework for performance evaluation of polyphonic sound event detection (SED) systems, which overcomes the limitations of the conventional collar-based eve…