标题： IS${}^3$ : 使用深度过滤的声学场景中通用的脉冲-稳态声音分离 显示英文标题

标题： IS${}^3$ : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering

摘要： 我们感兴趣的是能够对声景中的静止背景和孤立声学事件进行区分处理的音频系统，无论是在对每个部分应用特定处理方法时，还是仅关注其中一个而忽略另一个时。 此类系统在现实世界场景中有应用，包括稳健的自适应音频渲染系统（例如均衡器或压缩），语音混音中的爆破音衰减，噪声抑制或减少，稳健的声学事件分类甚至生物声学。 为此，我们引入了IS${}^3$，一种专为脉冲-静止声音分离设计的神经网络，它使用深度过滤方法将脉冲声学事件从静止背景中分离出来，可以作为上述任务的预处理阶段。 为了确保最佳训练，我们提出了一种复杂的数据生成流程，该流程整理并适应现有数据集以完成此任务。 我们证明，基于相对轻量级神经架构并使用精心设计和多样化数据训练的学习方法，在这一此前未涉及的任务中是成功的，其表现优于从音乐信号处理研究中改编的谐波-打击声分离掩码方法以及小波滤波在客观分离指标上的表现。 显示英文摘要

摘要： 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.