69 citations · 144 across the 22 of their papers we have counts for
6 papers · 1 filter
Towards Blind Data Cleaning: A Case Study in Music Source Separation
Azalea Gui, Woosung Choi, Junghyun Koo +5
The performance of deep learning models for music source separation heavily depends on training data quality. However, datasets are often corrupted by difficult-to-detect artifacts…
StereoSync: Spatially-Aware Stereo Audio Generation from Video
Christian Marinoni, Riccardo Fosco Gramaccioni, Kazuki Shimada +3
Although audio generation has been widely studied over recent years, video-aligned audio generation still remains a relatively unexplored frontier. To address this gap, we introduc…
SoundReactor: Frame-level Online Video-to-Audio Generation
Koichi Saito, Julian Tanke, Christian Simon +7
Prevailing Video-to-Audio (V2A) generation models operate offline, assuming an entire video sequence or chunks of frames are available beforehand. This critically limits their use…
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…
Combining Deterministic Enhanced Conditions with Dual-Streaming Encoding for Diffusion-Based Speech Enhancement
Hao Shi, Xugang Lu, Kazuki Shimada +1
Diffusion-based speech enhancement (SE) models need to incorporate correct prior knowledge as reliable conditions to generate accurate predictions. However, providing reliable cond…
CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural Audio Generation
Yuanhong Chen, Kazuki Shimada, Christian Simon +3
Binaural audio generation (BAG) aims to convert monaural audio to stereo audio using visual prompts, requiring a deep understanding of spatial and semantic information. However, cu…