用深度滤波分离声音场景中的突发声与背景音，提升音频处理精度。
IS${}^3$ : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering
- 基于深度滤波的神经网络，分离突发声与平稳背景音
- 在客观指标上优于传统方法和小波滤波
- 适合语音混音、噪声抑制等实际音频应用
本文关注能够对声音场景中的平稳背景与孤立声学事件进行差异化处理的音频系统，适用于特定处理或聚焦其中一类。该技术可用于鲁棒自适应音频渲染（如均衡器或压缩）、语音混音中爆破音抑制、噪声抑制或减少、鲁棒声学事件分类，甚至生物声学分析。为此，我们提出 IS³，一种用于突发-平稳声音分离的神经网络，通过深度滤波方法从平稳背景中分离突发声事件，可作为上述任务的预处理阶段。为实现最佳训练，我们设计了一套复杂的数据生成流程，对现有数据集进行裁剪与适配。实验表明，基于轻量级神经架构并使用精心设计的多样化数据训练的学习方法，在此前未被解决的任务上表现优异，超越了从音乐信号处理中借鉴的谐波-打击声分离掩码方法和小波滤波，在客观分离指标上取得更优效果。
原文摘要 · Abstract (English)
We are interested in audio systems capable of performing a differentiated processing of stationary backgrounds and isolated acoustic events within an acoustic scene, whether for applying specific processing methods to each part or for focusing solely on one while ignoring the other. Such systems have applications in real-world scenarios, including robust adaptive audio rendering systems (e.g., EQ or compression), plosive attenuation in voice mixing, noise suppression or reduction, robust acoustic event classification or even bioacoustics. To this end, we introduce IS${}^3$, a neural network designed for Impulsive--Stationary Sound Separation, that isolates impulsive acoustic events from the stationary background using a deep filtering approach, that can act as a pre-processing stage for the above-mentioned tasks. To ensure optimal training, we propose a sophisticated data generation pipeline that curates and adapts existing datasets for this task. We demonstrate that a learning-based approach, build on a relatively lightweight neural architecture and trained with well-designed and varied data, is successful in this previously unaddressed task, outperforming the Harmonic--Percussive Sound Separation masking method, adapted from music signal processing research, and wavelet filtering on objective separation metrics.
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