Content-based Music Similarity with Triplet Networks

Autor:	Cleveland, Joseph, Cheng, Derek, Zhou, Michael, Joachims, Thorsten, Turnbull, Douglas
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	We explore the feasibility of using triplet neural networks to embed songs based on content-based music similarity. Our network is trained using triplets of songs such that two songs by the same artist are embedded closer to one another than to a third song by a different artist. We compare two models that are trained using different ways of picking this third song: at random vs. based on shared genre labels. Our experiments are conducted using songs from the Free Music Archive and use standard audio features. The initial results show that shallow Siamese networks can be used to embed music for a simple artist retrieval task.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.04938 Zobrazit plný text záznamu View this record from Arxiv