Audio Cover Song Identification and Similarity: Background, Approaches, Evaluation, and Beyond

The purpose of this study: 1) To find out copyright violations in the field of music in the form of a cover song by recording through social media connected with Law Number 28 of 2014 concerning Copyright. 2) To find out the factors that influence copyright violations in the music field in the form of cover songs by recording through social media. The writing of this thesis uses a statute approach, by reviewing amendments to Law Number 28 of 2014 concerning Copyright and its implications for copyright and its implementation by state institutions and the Republic of Indonesia Law No. 19 of 2016 concerning Amendment of Law Number 11 Year 2008 Regarding Information and Electronic Transactions. Results of research conducted by the author: When someone does a cover song through social media without permission from the creator, both for the purpose not for commercial or commercial purposes, it is an infringement of copyright. Factors that influence the occurrence of violations of copyright in Indonesia include: 1) Weak law enforcement against violators. 2) Works on the internet can easily be duplicated and disseminated globally in a short period of time and in large quantities. 3) There is no limit on the place of the offender because a domain name or website can be accessed by anyone globally. 4) Procedures for events between countries in dealing with violations of copyright on the internet, such as to determine who the perpetrators are and when they occur and determine the jurisdiction of violations still vary.

Get full-text (via PubEx)

Enhanced Feature Summarizing for Effective Cover Song Identification

IEEE/ACM Transactions on Audio Speech and Language Processing ◽

10.1109/taslp.2019.2942157 ◽

2019 ◽

Vol 27 (12) ◽

pp. 2113-2126

Author(s):

Jingyi Hu ◽

Ning Chen

Keyword(s):

Cover Song

Get full-text (via PubEx)

Supervised Deep Hashing for Highly Efficient Cover Song Detection

2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) ◽

10.1109/mipr.2019.00049 ◽

2019 ◽

Author(s):

Zhaoqin Ye ◽

Jaeyoung Choi ◽

Gerald Friedland

Keyword(s):

Highly Efficient ◽

Deep Hashing ◽

Cover Song

Get full-text (via PubEx)

Similarity fusion scheme for cover song identification

Electronics Letters ◽

10.1049/el.2015.4013 ◽

2016 ◽

Vol 52 (13) ◽

pp. 1173-1175 ◽

Cited By ~ 8

Author(s):

Ning Chen ◽

Hai‐dong Xiao

Keyword(s):

Cover Song ◽

Fusion Scheme

Get full-text (via PubEx)

Cover song identification with 2D Fourier Transform sequences

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2017.7952229 ◽

2017 ◽

Cited By ~ 10

Author(s):

Prem Seetharaman ◽

Zafar Rafii

Keyword(s):

Fourier Transform ◽

Cover Song

Get full-text (via PubEx)

KaraMIR: A Project for Cover Song Identification and Singing Voice Analysis Using a Karaoke Songs Dataset

International Journal of Semantic Computing ◽

10.1142/s1793351x18400202 ◽

2018 ◽

Vol 12 (04) ◽

pp. 501-522

Author(s):

Ladislav Maršík ◽

Petr Martišek ◽

Jaroslav Pokorný ◽

Martin Rusek ◽

Kateřina Slaninová ◽

...

Keyword(s):

Dynamic Time Warping ◽

Singing Voice ◽

Time Warping ◽

Voice Analysis ◽

Audio Features ◽

Chord Distance ◽

Song Analysis ◽

Cover Song ◽

Dynamic Time ◽

Cover Songs

We introduce KaraMIR, a musical project dedicated to karaoke song analysis. Within KaraMIR, we define Kara1k, a dataset composed of 1000 cover songs provided by Recisio Karafun application, and the corresponding 1000 songs by the original artists. Kara1k is mainly dedicated toward cover song identification and singing voice analysis. For both tasks, Kara1k offers novel approaches, as each cover song is a studio-recorded song with the same arrangement as the original recording, but with different singers and musicians. Essentia, harmony-analyser, Marsyas, Vamp plugins and YAAFE have been used to extract audio features for each track in Kara1k. We provide metadata such as the title, genre, original artist, year, International Standard Recording Code and the ground truths for the singer’s gender, backing vocals, duets, and lyrics’ language. KaraMIR project focuses on defining new problems and describing features and tools to solve them. We thus provide a comparison of traditional and new features for a cover song identification task using statistical methods, as well as the dynamic time warping method on chroma, MFCC, chords, keys, and chord distance features. A supporting experiment on the singer gender classification task is also proposed. The KaraMIR project website facilitates the continuous research.

Get full-text (via PubEx)

A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing

Applied Sciences ◽

10.3390/app8081383 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1383 ◽

Cited By ~ 2

Author(s):

Mingyu Li ◽

Ning Chen

Keyword(s):

Information Retrieval ◽

State Of The Art ◽

Identification System ◽

Post Processing ◽

Similarity Functions ◽

Processing Level ◽

Retrieval Scheme ◽

The Common ◽

Cover Song ◽

Music Information

Similarity measurement plays an important role in various information retrieval tasks. In this paper, a music information retrieval scheme based on two-level similarity fusion and post-processing is proposed. At the similarity fusion level, to take full advantage of the common and complementary properties among different descriptors and different similarity functions, first, the track-by-track similarity graphs generated from the same descriptor but different similarity functions are fused with the similarity network fusion (SNF) technique. Then, the obtained first-level fused similarities based on different descriptors are further fused with the mixture Markov model (MMM) technique. At the post-processing level, diffusion is first performed on the two-level fused similarity graph to utilize the underlying track manifold contained within it. Then, a mutual proximity (MP) algorithm is adopted to refine the diffused similarity scores, which helps to reduce the bad influence caused by the “hubness” phenomenon contained in the scores. The performance of the proposed scheme is tested in the cover song identification (CSI) task on three cover song datasets (Covers80, Covers40, and Second Hand Songs (SHS)). The experimental results demonstrate that the proposed scheme outperforms state-of-the-art CSI schemes based on single similarity or similarity fusion.

Get full-text (via PubEx)