While audio quality is a key performance metric for various audio processing tasks, including generative modeling, its objective measurement remains a challenge. Audio-Language Models (ALMs) are pre-trained on audio-text pairs that may contain information about audio quality, the presence of artifacts, or noise. Given an audio input and a text prompt related to quality, an ALM can be used to calculate a similarity score between the two. Here, we exploit this capability and introduce PAM, a no-reference metric for assessing audio quality for different audio processing tasks. Contrary to other "reference-free" metrics, PAM does not require computing embeddings on a reference dataset nor training a task-specific model on a costly set of human listening scores. We extensively evaluate the reliability of PAM against established metrics and human listening scores on four tasks: text-to-audio (TTA), text-to-music generation (TTM), text-to-speech (TTS), and deep noise suppression (DNS). We perform multiple ablation studies with controlled distortions, in-the-wild setups, and prompt choices. Our evaluation shows that PAM correlates well with existing metrics and human listening scores. These results demonstrate the potential of ALMs for computing a general-purpose audio quality metric.

翻译：音频质量是包括生成建模在内的多种音频处理任务的关键性能指标，但客观测量仍是一大挑战。音频-语言模型（ALMs）通过音频-文本对进行预训练，其中可能包含关于音频质量、伪影存在或噪声的信息。给定音频输入和与质量相关的文本提示，ALM可用于计算两者之间的相似度得分。本文利用这一能力，提出一种无参考指标PAM，用于评估不同音频处理任务中的音频质量。与其他"无参考"指标不同，PAM无需在参考数据集上计算嵌入，也无需基于昂贵的听力评分数据集训练任务特定模型。我们在四项任务上广泛评估了PAM相对于既有指标和人类听力评分的可靠性：文本到音频（TTA）、文本到音乐生成（TTM）、文本到语音（TTS）以及深度噪声抑制（DNS）。我们进行了多项消融实验，包括受控失真、野外场景及提示选择。评估结果表明，PAM与既有指标和人类听力评分具有良好相关性，展现了ALM在计算通用音频质量指标方面的潜力。