3 accepted papers
In this paper, we propose a novel audio declipping method that fuses sparse-optimization-based and deep neural network (DNN)– based methods. The two methods have contrasting characteristics, depending on clipping level. Sparse-optimization-based audio de-clipping can preserve reliable samples, being…
In this paper, we propose an audio declipping method that takes advantages of both sparse optimization and deep learning. Since sparsity-based audio declipping methods have been developed upon constrained optimization, they are adjustable and well-studied in theory. However, they always uniformly pr…
Phase reconstruction from amplitude spectrograms has attracted attention in recent acoustics because of its potential applications in speech synthesis and enhancement. The most well-known algorithm in acoustics is based on alternating projection and called Griffin– Lim algorithm (GLA). At the same t…