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20162026
most citedSound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks

537 citations · 835 across the 48 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.SD2025★ 1 cited

Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification

Kazuki Shimada, Archontis Politis, Iran R. Roman +10

This paper presents the objective, dataset, baseline, and metrics of Task 3 of the DCASE2025 Challenge on sound event localization and detection (SELD). In previous editions, the c…

cs.SD2025

Towards Spatial Audio Understanding via Question Answering

Parthasaarathy Sudarsanam, Archontis Politis

In this paper, we introduce a novel framework for spatial audio understanding of first-order ambisonic (FOA) signals through a question answering (QA) paradigm, aiming to extend th…

eess.AS2025

Multi-Utterance Speech Separation and Association Trained on Short Segments

Yuzhu Wang, Archontis Politis, Konstantinos Drossos +1

Current deep neural network (DNN) based speech separation faces a fundamental challenge -- while the models need to be trained on short segments due to computational constraints, r…

eess.AS2025

Inter-Speaker Relative Cues for Text-Guided Target Speech Extraction

Wang Dai, Archontis Politis, Tuomas Virtanen

We propose a novel approach that utilizes inter-speaker relative cues to distinguish target speakers and extract their voices from mixtures. Continuous cues (e.g., temporal order,…

eess.AS2025

Attractor-Based Speech Separation of Multiple Utterances by Unknown Number of Speakers

Yuzhu Wang, Archontis Politis, Konstantinos Drossos +1

This paper addresses the problem of single-channel speech separation, where the number of speakers is unknown, and each speaker may speak multiple utterances. We propose a speech s…

eess.AS2025

Score-informed Music Source Separation: Improving Synthetic-to-real Generalization in Classical Music

Eetu Tunturi, David Diaz-Guerra, Archontis Politis +1

Music source separation is the task of separating a mixture of instruments into constituent tracks. Music source separation models are typically trained using only audio data, alth…

eess.AS2025

Gen-A: Generalizing Ambisonics Neural Encoding to Unseen Microphone Arrays

Mikko Heikkinen, Archontis Politis, Konstantinos Drossos +1

Using deep neural networks (DNNs) for encoding of microphone array (MA) signals to the Ambisonics spatial audio format can surpass certain limitations of established conventional m…