Data-driven audio recognition: a supervised dictionary approach
This work provides a generic and data-driven framework for machine listening applications, potentially benefiting researchers and practitioners in audio recognition.
This paper proposes a data-driven representation learning approach for audio recognition. The method achieves state-of-the-art performance compared to hand-crafted features on both computational auditory scene and synthetic music chord recognition datasets.
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