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cs.SD2025
Spatial-CLAP: Learning Spatially-Aware audio--text Embeddings for Multi-Source Conditions
Kentaro Seki, Yuki Okamoto, Kouei Yamaoka +3
Contrastive language--audio pretraining (CLAP) has achieved remarkable success as an audio--text embedding framework, but existing approaches are limited to monaural or single-sour…
cs.SD2025
RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio
Yusuke Kanamori, Yuki Okamoto, Taisei Takano +3
In text-to-audio (TTA) research, the relevance between input text and output audio is an important evaluation aspect. Traditionally, it has been evaluated from both subjective and…
eess.AS2025
Human-CLAP: Human-perception-based contrastive language-audio pretraining
Taisei Takano, Yuki Okamoto, Yusuke Kanamori +3
Contrastive language-audio pretraining (CLAP) is widely used for audio generation and recognition tasks. For example, CLAPScore, which utilizes the similarity of CLAP embeddings, h…