13 papers
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…
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…
Mind the Microphone Gap: Benchmarking Array Upsampling Strategies for Latent Acoustic Mapping
Philipp Schmidt, Huw Cheston, Juan Azcarreta +3
Latent Acoustic Mapping (LAM) is a self-supervised learning method that generates high-resolution spherical acoustic maps from multichannel recordings without labelled data, matchi…
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…
Efficient Neural and Numerical Methods for High-Quality Online Speech Spectrogram Inversion via Gradient Theorem
Andres Fernandez, Juan Azcarreta, Cagdas Bilen +1
Recent work in online speech spectrogram inversion effectively combines Deep Learning with the Gradient Theorem to predict phase derivatives directly from magnitudes. Then, phases…
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…
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…
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…
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…