论文标题
数据驱动的音频识别：一种有监督的词典方法
Data-driven audio recognition: a supervised dictionary approach
论文作者
论文摘要
机器听力是一个新兴区域。由于需要跨域应用程序进行机器侦听的原则框架，我们提出了一种通用和数据驱动的表示方法。为此，提出了一种新颖有效的监督词典学习方法。实验在计算听觉场景（East Anglia和Rouen）和合成音乐和弦识别数据集上进行。获得的结果表明，我们的方法能够达到两种应用的最新手工制作的功能
Machine hearing is an emerging area. Motivated by the need of a principled framework across domain applications for machine listening, we propose a generic and data-driven representation learning approach. For this sake, a novel and efficient supervised dictionary learning method is presented. Experiments are performed on both computational auditory scene (East Anglia and Rouen) and synthetic music chord recognition datasets. Obtained results show that our method is capable to reach state-of-the-art hand-crafted features for both applications
