Semantic Learning for Audio Applications: A Computer Vision Approach

Recent work in machine learning has significantly benefited semantic extraction tasks in computer vision, particularly for object recognition and image retrieval. We argue that the computer vision techniques that have been successfully applied in those settings can effectively be translated to other domains, such as audio. This claim is supported by recent results in music vs. speech classification, structure from sound, robust music identification and sound object recognition. This paper focuses on two such audio applications and demonstrates how ideas from computer vision map naturally to these problems.