Sorry, you need to enable JavaScript to visit this website.

COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK

Citation Author(s):
Sungkyun Chang, Sangkeun Choe, Kyogu Lee
Submitted by:
Juheon Lee
Last updated:
13 April 2018 - 12:15am
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Juheon Lee
Paper Code:
2284
 

In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-similarity matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output–the cover-probabilities of one song to all other candidate songs–as a new representation vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the representation vectors; 2. the cosine distance between the representation vectors; and 3. the correlation between the vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.

up
0 users have voted: