论文标题
音频信号的一声声匹配 - 学会在任何房间/音乐厅听音乐
One-Shot Acoustic Matching Of Audio Signals -- Learning to Hear Music In Any Room/ Concert Hall
论文作者
论文摘要
通过提供独特的\ textit {stricement}的独特感，可以创建声音并听到声音的声学空间在如何看待声音中起着至关重要的作用。我们听到的每一个声音都来自连续的卷积操作的固有的固有的固有性和外部因素，例如麦克风特征和房间冲动响应。通常，研究人员使用诸如手枪射击或气球弹出的激发作为脉冲信号，可以通过该信号来创建听觉化。房间“脉冲”的响应与感兴趣的信号卷曲可以将输入声音转化为在感兴趣的声学空间中播放的声音。在这里，我们提出了一种新颖的体系结构，可以将有趣的声音转变为其他观念空间（房间或大厅），通过使用任意音频记录为气球流行音乐的代理。该体系结构以简单的信号处理想法为基础，以从学习的声学签名和输入信号中学习残留信号。我们的框架允许神经网络调整时间频表示的每个点的收益，从而为声音定性和定量结果。
The acoustic space in which a sound is created and heard plays an essential role in how that sound is perceived by affording a unique sense of \textit{presence}. Every sound we hear results from successive convolution operations intrinsic to the sound source and external factors such as microphone characteristics and room impulse responses. Typically, researchers use an excitation such as a pistol shot or balloon pop as an impulse signal with which an auralization can be created. The room "impulse" responses convolved with the signal of interest can transform the input sound into the sound played in the acoustic space of interest. Here we propose a novel architecture that can transform a sound of interest into any other acoustic space(room or hall) of interest by using arbitrary audio recorded as a proxy for a balloon pop. The architecture is grounded in simple signal processing ideas to learn residual signals from a learned acoustic signature and the input signal. Our framework allows a neural network to adjust gains of every point in the time-frequency representation, giving sound qualitative and quantitative results.
