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Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
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
Sungkyun Chang, Juheon Lee
exaly   +2 more sources

Deep feature learning for cover song identification

Multimedia Tools and Applications, 2016
The identification of a cover song, which is an alternative version of a previously recorded song, for music retrieval has received increasing attention. Methods for identifying a cover song typically involve comparing the similarity of chroma features between a query song and another song in the data set.
Pao-Chi Chang, Chang Pao-Chi
exaly   +2 more sources

Deep Learning for Cover Song Apperception

2020
In this work, we proposed a cover song recognition system using deep learning. From the literature, understand that most of the works extract the discriminate feature that classifies the cover song between a pair of songs and calculates the dissimilarity or similarity between the two songs based on the observation, which is a meaningful pattern between
D. Khasim Vali, Nagappa U. Bhajantri
openaire   +1 more source

In Defense of Cover Songs

Popular Music and Society, 2005
The music industry of the late 20th and eraly 21st century has been enamoured by the singer–songwriter; this has caused critics and fans to dismiss the singer who “covers” songs as a less legitimate artist. “In Defense of Cover Songs” argues that the singer who sings songs written by others is also a legitimate artist and that cover songs represent a ...
openaire   +1 more source

Cover Songs

2013
Documents submitted to the Faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Master of Fine Arts in the Department of Art.
openaire   +1 more source

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