标题： 通过三元组损失和自监督嵌入改进感知音频美学评估 显示英文标题

标题： Improving Perceptual Audio Aesthetic Assessment via Triplet Loss and Self-Supervised Embeddings

摘要： 我们提出了一种用于生成音频的多轴感知质量预测的系统，该系统专为AudioMOS挑战2025年的Track 2而开发。 任务是对由文本到语音（TTS）、文本到音频（TTA）和文本到音乐（TTM）系统生成的音频预测四个音频审美评分——制作质量、制作复杂性、内容愉悦度和内容实用性。 主要挑战是自然训练数据与合成评估数据之间的领域偏移。 为了解决这个问题，我们将BEATs（一种预训练的基于变压器的音频表示模型）与多分支长短期记忆（LSTM）预测器相结合，并使用带有缓冲区采样的三元组损失来通过感知相似性构建嵌入空间。 我们的结果表明，这提高了嵌入的可区分性和泛化能力，使无需合成训练数据的领域稳健音频质量评估成为可能。 显示英文摘要

摘要： We present a system for automatic multi-axis perceptual quality prediction of generative audio, developed for Track 2 of the AudioMOS Challenge 2025. The task is to predict four Audio Aesthetic Scores--Production Quality, Production Complexity, Content Enjoyment, and Content Usefulness--for audio generated by text-to-speech (TTS), text-to-audio (TTA), and text-to-music (TTM) systems. A main challenge is the domain shift between natural training data and synthetic evaluation data. To address this, we combine BEATs, a pretrained transformer-based audio representation model, with a multi-branch long short-term memory (LSTM) predictor and use a triplet loss with buffer-based sampling to structure the embedding space by perceptual similarity. Our results show that this improves embedding discriminability and generalization, enabling domain-robust audio quality assessment without synthetic training data.