Zero-Shot Denoiser for Enhanced Acoustic Inspection: Blind Signal Separation and Text-Guided Audio Reconstruction

Abstract

Acoustic inspection is crucial for infrastructure maintenance, but its effectiveness is often hampered by environmental noise. Conventional denoising methods rely on prior knowledge or training data, limiting their practicability. This paper presents Zero-Shot Denoiser, a novel approach achieving noise reduction without pre-collected target sound samples or noise knowledge. Our method synergistically combines Blind Signal Separation (BSS) for unsupervised audio decomposition and Artifact-Resilient Attention (AR-Attention) for text-guided audio reconstruction. AR-Attention leverages pre-trained audio-language models and dual normalization to mitigate BSS artifacts and identify target sounds semantically. We introduce pseudo Signal-to-Noise Ratio, derived from the audio-language model, for automatic BSS hyperparameter optimization. In experiments using public datasets, our method, operating in a true zero-shot setting, achieved performance comparable to that of state-of-the-art supervised denoising methods, and experiments targeting hammering tests confirmed the effectiveness of our approach for real-world acoustic inspections. Our approach overcomes the limitations of data-dependent techniques and offers a versatile noise reduction solution for acoustic inspection and broader acoustic tasks.