In this research, we present an innovative, parameter-efficient model that utilizes the attention U-Net architecture for the automatic detection and eradication of non-speech vocal sounds, specifically breath sounds, in vocal recordings. This task is of paramount importance in the field of sound engineering, despite being relatively under-explored. The conventional manual process for detecting and eliminating these sounds requires significant expertise and is extremely time-intensive. Existing automated detection and removal methods often fall short in terms of efficiency and precision. Our proposed model addresses these limitations by offering a streamlined process and superior accuracy, achieved through the application of advanced deep learning techniques. A unique dataset, derived from Device and Produced Speech (DAPS), was employed for this purpose. The training phase of the model emphasizes a log spectrogram and integrates an early stopping mechanism to prevent overfitting. Our model not only conserves precious time for sound engineers but also enhances the quality and consistency of audio production. This constitutes a significant breakthrough, as evidenced by its comparative efficiency, necessitating only 1.9M parameters and a training duration of 3.2 hours - markedly less than the top-performing models in this domain. The model is capable of generating identical outputs as previous models with drastically improved precision, making it an optimal choice.

本研究针对音频录音中的呼吸声去除问题，提出了一种创新的低参数模型，利用注意力U-Net架构实现自动检测与消除。该模型通过先进的深度学习技术，显著提升了效率与准确性，展现出显著的突破，使声音工程师节省时间，同时提高音频制作的质量与一致性。

基于注意力的高效呼吸声去除在录音室音频中的应用