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

中文翻译：

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数据驱动的音频识别：有监督的词典方法

机器听力是一个新兴领域。出于跨域应用程序中用于机器侦听的原则性框架的需要，我们提出了一种通用的，数据驱动的表示学习方法。为此，提出了一种新颖而有效的监督词典学习方法。在计算听觉场景（东安格利亚和鲁昂）和合成音乐和弦识别数据集上都进行了实验。所得结果表明，我们的方法能够针对两种应用达到最先进的手工制作功能