Zero-shot Learning for Audio-based Music Classification and Tagging

Understand

Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels.

• This intrinsically cannot handle unseen labels such as newly added music genres or semantic words that users arbitrarily choose for music retrieval.
• Zero-shot learning can address this problem by leveraging an additional semantic space of labels where side information about the labels is used to unveil the relationship between each other.
• In this work, we investigate the zero-shot learning in the music domain and organize two different setups of side information.