4 citations · 8 across the 7 of their papers we have counts for
7 papers
SALM: Spatial Audio Language Model with Structured Embeddings for Understanding and Editing
Jinbo Hu, Yin Cao, Ming Wu +2
Spatial audio understanding is essential for accurately perceiving and interpreting acoustic environments. However, existing audio-language models exhibit limitations in processing…
PSELDNets: Pre-trained Neural Networks on a Large-scale Synthetic Dataset for Sound Event Localization and Detection
Jinbo Hu, Yin Cao, Ming Wu +5
Sound event localization and detection (SELD) has seen substantial advancements through learning-based methods. These systems, typically trained from scratch on specific datasets,…
Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection
Jinbo Hu, Yin Cao, Ming Wu +4
Environment shifts and conflicts present significant challenges for learning-based sound event localization and detection (SELD) methods. SELD systems, when trained in particular a…
META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection
Jinbo Hu, Yin Cao, Ming Wu +5
For learning-based sound event localization and detection (SELD) methods, different acoustic environments in the training and test sets may result in large performance differences…
Sound Event Localization and Detection for Real Spatial Sound Scenes: Event-Independent Network and Data Augmentation Chains
Jinbo Hu, Yin Cao, Ming Wu +4
Sound event localization and detection (SELD) is a joint task of sound event detection and direction-of-arrival estimation. In DCASE 2022 Task 3, types of data transform from compu…
A Track-Wise Ensemble Event Independent Network for Polyphonic Sound Event Localization and Detection
Jinbo Hu, Yin Cao, Ming Wu +4
Polyphonic sound event localization and detection (SELD) aims at detecting types of sound events with corresponding temporal activities and spatial locations. In this paper, a trac…