arXiv subjects

Taisei Takano

Publications and source records attributed to Taisei Takano.

3 recordsLinked to original sources

arXiv2026

Human-CLAP: Human-perception-based contrastive language-audio pretraining

Contrastive language-audio pretraining (CLAP) is widely used for audio generation and recognition tasks. For example, CLAPScore, which utilizes the similarity of CLAP embeddings, has been a major metric for the evaluation of the relevance between audio and text in text-to-audio. However, the relationship between CLAPScore and human subjective evaluation scores is still unclarified. We show that CLAPScore has a low correlation with human subjective evaluation scores. Additionally, we propose a human-perception-based CLAP called Human-CLAP by training a contrastive language-audio model using the subjective evaluation score. In our experiments, the results indicate that our Human-CLAP improved the Spearman's rank correlation coefficient (SRCC) between the CLAPScore and the subjective evaluation scores by more than 0.25 compared with the conventional CLAP.

eess.AS↗

arXiv2026

SPO-CLAPScore: Enhancing CLAP-based alignment prediction system with Standardize Preference Optimization, for the first XACLE Challenge

The first XACLE Challenge (x-to-audio alignment challenge) addresses the critical need for automatic evaluation metrics that correlate with human perception of audio-text semantic alignment. In this paper, we describe the "Takano_UTokyo_03" system submitted to XACLE Challenge. Our approach leverages a CLAPScore-based architecture integrated with a novel training method called Standardized Preference Optimization (SPO). SPO standardizes the raw alignment scores provided by each listener, enabling the model to learn relative preferences and mitigate the impact of individual scoring biases. Additionally, we employ listener screening to exclude listeners with inconsistent ratings. Experimental evaluations demonstrate that both SPO and listener screening effectively improve the correlation with human judgment. Our system achieved 6th place in the challenge with a Spearman's rank correlation coefficient (SRCC) of 0.6142, demonstrating competitive performance within a marginal gap from the top-ranked systems. The code is available at https://github.com/ttakano398/SPO-CLAPScore.

cs.SD↗

arXiv2025

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio

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 objective perspectives. However, subjective evaluation is costly in terms of money and time, and objective evaluation is unclear regarding the correlation to subjective evaluation scores. In this study, we construct RELATE, an open-sourced dataset that subjectively evaluates the relevance. Also, we benchmark a model for automatically predicting the subjective evaluation score from synthesized audio. Our model outperforms a conventional CLAPScore model, and that trend extends to many sound categories.

cs.SD↗