Problem
Research questions and friction points this paper is trying to address.
Time-Varying Exponentiated Convolution
Spherical Harmonic
Reverberation
Data-Set Augmentation
Synthetic Data Generation
Fast Time-Varying Exponentiated Convolution Methods for Generative Direction Dependent Reverberation
🤖 AI Summary
本文提出了一种时变指数卷积方法，用于生成方向依赖的混响和修改频谱衰减场，以解决多麦克风测量成本高和数据集小的问题。
📝 Abstract
Spherical harmonic encoded acoustic sound-fields capture directional characteristics of room impulse responses that are useful for accurate spatial audio reproduction. However, high costs of multi-microphone measurements and numerical simulations motivate alternative data-set augmentation and synthetic data generation methods that supplement small collections. This paper introduces time-varying exponentiated convolution methods that transform both Gaussian noise and impulse responses into reverberation and modified spectral-decay fields respectively. We derive two recursive and fast convolution algorithms that extend into the spherical harmonic domain, model smooth reverberation time distributions with non-stationary Gaussian processes, and realize an optimal filter design. Experiments evaluate computational performance, and validate out-of-distribution generated impulse responses.
Innovation
Methods, ideas, or system contributions that make the work stand out.
time-varying exponentiated convolution
spherical harmonic domain
non-stationary Gaussian processes
optimal filter design
🔎 Similar Papers
No similar papers found.
