5 accepted papers
Neural networks have been widely applied in direction-of-arrival (DOA) estimation and source tracking systems. In this paper, we introduce a spherical convolutional recurrent neural network that utilizes Deepsphere, a graph-based spherical convolutional neural network, employing the steered response…
This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform domain. Time-frequency (T-F) mask processing in the short-time Fourier transform (STFT)-domain is a typical speech enhan…
A method to create multipoles comprising a cluster of focused sources by using a linear loudspeaker array has recently been investigated. Directivities in a listening area were confirmed with examples of primitive multipoles such as dipoles and quadrupoles. This paper describes a method to create a…
This paper presents an end-to-end deep neural network (DNN)-based source enhancement on the basis of a time-frequency (T-F) mask processing in the modified discrete cosine transform (MDCT)-domain. To retrieve the target signal perfectly in the discrete Fourier transform (DFT)-domain, both amplitude…
We investigated whether a deep neural network (DNN)-based source enhancement function can be self-optimized by reinforcement learning (RL). The use of a DNN is a powerful approach to describing the relationship between two sets of variables and can be useful for source enhancement function design. B…