Attention-Based Efficient Breath Sound Removal in Studio Audio Recordings

TL;DR

This paper introduces an attention U-Net based model for efficient, accurate removal of breath sounds from vocal recordings, significantly reducing training time and model complexity while improving precision.

Contribution

The study presents a novel, parameter-efficient deep learning model that outperforms existing methods in breath sound removal using a specialized dataset and attention mechanisms.

Findings

Model requires only 1.9M parameters and 3.2 hours of training.

Achieves higher precision than previous models.

Reduces manual effort and enhances audio quality.

Abstract

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.