11 citations · 16 across the 14 of their papers we have counts for
16 papers
GLAP: General contrastive audio-text pretraining across domains and languages
Heinrich Dinkel, Zhiyong Yan, Tianzi Wang +7
Contrastive Language Audio Pretraining (CLAP) is a widely-used method to bridge the gap between audio and text domains. Current CLAP methods enable sound and music retrieval in Eng…
Enhancing Automated Audio Captioning via Large Language Models with Optimized Audio Encoding
Jizhong Liu, Gang Li, Junbo Zhang +5
Automated audio captioning (AAC) is an audio-to-text task to describe audio contents in natural language. Recently, the advancements in large language models (LLMs), with improveme…
Bridging Language Gaps in Audio-Text Retrieval
Zhiyong Yan, Heinrich Dinkel, Yongqing Wang +4
Audio-text retrieval is a challenging task, requiring the search for an audio clip or a text caption within a database. The predominant focus of existing research on English descri…
Scaling up masked audio encoder learning for general audio classification
Heinrich Dinkel, Zhiyong Yan, Yongqing Wang +3
Despite progress in audio classification, a generalization gap remains between speech and other sound domains, such as environmental sounds and music. Models trained for speech tas…
CED: Consistent ensemble distillation for audio tagging
Heinrich Dinkel, Yongqing Wang, Zhiyong Yan +2
Augmentation and knowledge distillation (KD) are well-established techniques employed in audio classification tasks, aimed at enhancing performance and reducing model sizes on the…
AV-SepFormer: Cross-Attention SepFormer for Audio-Visual Target Speaker Extraction
Jiuxin Lin, Xinyu Cai, Heinrich Dinkel +7
Visual information can serve as an effective cue for target speaker extraction (TSE) and is vital to improving extraction performance. In this paper, we propose AV-SepFormer, a Sep…