PAM: Prompting Audio-Language Models for Audio Quality Assessment

Soham Deshmukh; Dareen Alharthi; Benjamin Elizalde; Hannes Gamper; Mahmoud Al Ismail; Rita Singh; Bhiksha Raj; Huaming Wang

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arXiv · 2402.00282

PAM: Prompting Audio-Language Models for Audio Quality Assessment

Soham Deshmukh·Dareen Alharthi·Benjamin Elizalde·Hannes Gamper·Mahmoud Al Ismail·Rita Singh·Bhiksha Raj·Huaming Wang

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Abstract

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.

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BibTeX RIS

Soham Deshmukh, Dareen Alharthi, Benjamin Elizalde, Hannes Gamper, Mahmoud Al Ismail, Rita Singh, Bhiksha Raj, Huaming Wang. 2024-02-01. PAM: Prompting Audio-Language Models for Audio Quality Assessment. https://arxiv.org/abs/2402.00282

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