Surgical scene understanding is a prerequisite for context-aware and intelligent systems in the operating room. Current methods rely mostly on visual data, although sound carries complementary information on surgical activity. This work investigates whether the active instrument can be localised from a single microphone and a camera by learning the correspondence between the appearance and sound of surgical instruments. We adapt weakly supervised audio-visual localisation, which learns from the co-occurrence of image and sound, to the surgical setting. Trained without any spatial annotation, the model predicts a heatmap over the sounding instrument. We extend its training objective by silence and area terms, which suppress the output for silence and background noise, and a composite term, which trains the model to select the sounding one of two visible instruments. For training, we record a new corpus of simulated surgical sawing, drilling, and chiselling. On an independent corpus recorded with a different camera and microphone and with optically tracked instrument positions, the model localises the active instrument, separates instrument activity from silence and background noise, and attributes sound correctly when two instruments are present. Ablations show that the silence and area terms are required for detecting activity, and the composite term for selecting between instruments. Audio-visual correspondence learned from in-domain recordings connects the sound of a surgical action to the instrument that produces it. This proof of concept introduces a novel paradigm that enables the detection and localisation of surgical activity without annotating instrument positions.

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