Semi-intrusive audio evaluation: Casting non-intrusive assessment as a multi-modal text prediction task

Jozef Coldenhoff; Milos Cernak

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arXiv · 2409.14069

Semi-intrusive audio evaluation: Casting non-intrusive assessment as a multi-modal text prediction task

Jozef Coldenhoff·Milos Cernak

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Abstract

Human perception has the unique ability to focus on specific events in a mixture of signals--a challenging task for existing non-intrusive assessment methods. In this work, we introduce semi-intrusive assessment that emulates human attention by framing audio assessment as a text-prediction task with audio-text inputs. To this end, we extend the multi-modal PENGI model through instruction fine-tuning for MOS and SNR estimation. For MOS, our approach achieves absolute Pearson correlation gains of 0.06 and 0.20 over the re-trained MOSRA model and the pre-trained PAM model, respectively. We further propose a novel SNR estimator that can focus on a specific audio source in a mixture, outperforming a random baseline and the fixed-prompt counterpart. Our findings suggest that semi-intrusive assessment can effectively capture human-like selective listening capabilities. Samples are available at https://jozefcoldenhoff.github.io/semi-intrusive-assessment.

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BibTeX RIS

Jozef Coldenhoff, Milos Cernak. 2024-09-21. Semi-intrusive audio evaluation: Casting non-intrusive assessment as a multi-modal text prediction task. https://arxiv.org/abs/2409.14069

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