# Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification arXiv: https://arxiv.org/abs/2507.12042 ยท HTML: https://mlsanity.com/paper/2507.12042/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 34 references that are in the map. - First posted on arXiv 16 Jul 2025. ## Builds on (most cited first, 30 of 30) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020) - [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016) - [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018) - [STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events](https://mlsanity.com/paper/2306.09126/) (2023) - [Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019](https://mlsanity.com/paper/2009.02792/) (2020) - [ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection](https://mlsanity.com/paper/2010.15306/) (2020) - [An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2010.13092/) (2020) - [Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training](https://mlsanity.com/paper/2110.07124/) (2021) - [A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection](https://mlsanity.com/paper/2006.01919/) (2020) - [A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection](https://mlsanity.com/paper/2101.02919/) (2021) - [A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection](https://mlsanity.com/paper/2106.06999/) (2021) - [Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy](https://mlsanity.com/paper/1905.00268/) (2019) - [A multi-room reverberant dataset for sound event localization and detection](https://mlsanity.com/paper/1905.08546/) (2019) - [STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events](https://mlsanity.com/paper/2206.01948/) (2022) - [Sound Event Detection and Localization with Distance Estimation](https://mlsanity.com/paper/2403.11827/) (2024) - [First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation](https://mlsanity.com/paper/1910.04388/) (2019) - [Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic Rooms](https://mlsanity.com/paper/2401.12238/) (2024) - [A Sequence Matching Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2002.05865/) (2020) - [SALSA-Lite: A Fast and Effective Feature for Polyphonic Sound Event Localization and Detection with Microphone Arrays](https://mlsanity.com/paper/2111.08192/) (2021) - [SAVGBench: Benchmarking Spatially Aligned Audio-Video Generation](https://mlsanity.com/paper/2412.13462/) (2024) - [Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection](https://mlsanity.com/paper/2312.09034/) (2023) - [Sound source detection, localization and classification using consecutive ensemble of CRNN models](https://mlsanity.com/paper/1908.00766/) (2019) - [Sound Source Distance Estimation in Diverse and Dynamic Acoustic Conditions](https://mlsanity.com/paper/2309.09288/) (2023) - [Assessment of Self-Attention on Learned Features For Sound Event Localization and Detection](https://mlsanity.com/paper/2107.09388/) (2021) - [Two vs. Four-Channel Sound Event Localization and Detection](https://mlsanity.com/paper/2309.13343/) (2023) - [Generating Diverse Audio-Visual 360 Soundscapes for Sound Event Localization and Detection](https://mlsanity.com/paper/2504.02988/) (2025) - [Spatial Data Augmentation with Simulated Room Impulse Responses for Sound Event Localization and Detection](https://mlsanity.com/paper/2110.06501/) (2021) - [Sound Event Localization and Detection for Real Spatial Sound Scenes: Event-Independent Network and Data Augmentation Chains](https://mlsanity.com/paper/2209.01802/) (2022) ## Built on by (most cited first, 4 of 4) - [Spatial Audio Question Answering and Reasoning on Dynamic Source Movements](https://mlsanity.com/paper/2602.16334/) (2026) - [Spatial Audio Motion Understanding and Reasoning](https://mlsanity.com/paper/2509.14666/) (2025) - [SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios](https://mlsanity.com/paper/2607.02343/) (2026) - [ToS: A Team of Specialists ensemble framework for Stereo Sound Event Localization and Detection with distance estimation in Video](https://mlsanity.com/paper/2601.17611/) (2026) ## Abstract (arXiv) 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 challenge used four-channel audio formats of first-order Ambisonics (FOA) and microphone array. In contrast, this year's challenge investigates SELD with stereo audio data (termed stereo SELD). This change shifts the focus from more specialized 360deg audio and audiovisual scene analysis to more commonplace audio and media scenarios with limited field-of-view (FOV). Due to inherent angular ambiguities in stereo audio data, the task focuses on direction-of-arrival (DOA) estimation in the azimuth plane (left-right axis) along with distance estimation. The challenge remains divided into two tracks: audio-only and audiovisual, with the audiovisual track introducing a new sub-task of onscreen/offscreen event classification necessitated by the limited FOV. This challenge introduces the DCASE2025 Task3 Stereo SELD Dataset, whose stereo audio and perspective video clips are sampled and converted from the STARSS23 recordings. The baseline system is designed to process stereo audio and corresponding video frames as inputs. In addition to the typical SELD event classification and localization, it integrates onscreen/offscreen classification for the audiovisual track. The evaluation metrics have been modified to introduce an onscreen/offscreen accuracy metric, which assesses the models' ability to identify which sound sources are onscreen. In the experimental evaluation, the baseline system performs reasonably well with the stereo audio data.