Multi-level Stereo Attention Model for Center Channel Extraction
Wootaek Lım, Seungkwon Beack, Tae‐Jin Lee · 2019
In recent years, the spatial audio reproduction of digital media has become popular. Despite the demand for such spatial audio content, very little content is produced with multi-channel audio. Moreover, it is difficult to provide interactive services to users owing to the lack of object-based content. In this paper, we propose a center channel extraction method based on a multi-level convolutional neural network structure to generate object-based content. In addition, we present a novel stereo attention model which considers each channel's characteristics. By applying the proposed method to stereo audio content, we achieve better extraction performance than existing commercial application.