@Article{          malandrino.ea2022-adaptive,
    author       = {Malandrino, Delfina and {De Prisco}, Roberto and
                   Ianulardo, Mario and Zaccagnino, Rocco},
    year         = {2022},
    title        = {An adaptive meta-heuristic for music plagiarism detection
                   based on text similarity and clustering},
    abstract     = {Plagiarism is a controversial and debated topic in
                   different fields, especially in the Music one, where the
                   commercial market generates a huge amount of money. The
                   lack of objective metrics to decide whether a song is a
                   plagiarism, makes music plagiarism detection a very
                   complex task: often decisions have to be based on
                   subjective argumentations. Automated music analysis
                   methods that identify music similarities can be of help.
                   In this work, we first propose two novel such methods: a
                   text similarity-based method and a clustering-based
                   method. Then, we show how to combine them to get an
                   improved (hybrid) method. The result is a novel adaptive
                   meta-heuristic for music plagiarism detection. To assess
                   the effectiveness of the proposed methods, considered both
                   singularly and in the combined meta-heuristic, we
                   performed tests on a large dataset of ascertained
                   plagiarism and non-plagiarism cases. Results show that the
                   meta-heuristic outperforms existing methods. Finally, we
                   deployed the meta-heuristic into a tool , accessible as a
                   Web application, and assessed the effectiveness,
                   usefulness, and overall user acceptance of the tool by
                   means of a study involving 20 people, divided into two
                   groups, one of which with access to the tool. The study
                   consisted in having people decide which pair of songs, in
                   a predefined set of pairs, should be considered
                   plagiarisms and which not. The study shows that the group
                   supported by our tool successfully identified all
                   plagiarism cases, performing all tasks with no errors. The
                   whole sample agreed about the usefulness of an automatic
                   tool that provides a measure of similarity between two
                   songs.},
    doi          = {10.1007/s10618-022-00835-2},
    issn         = {1384-5810},
    journal      = {Data Mining and Knowledge Discovery},
    keywords     = {Clustering,Evaluation study,Multi-objective
                   optimization,Music plagiarism detection,Text
                   similarity,computational musicology},
    mendeley-tags= {computational musicology},
    month        = {may},
    publisher    = {Springer US},
    url          = {https://link.springer.com/10.1007/s10618-022-00835-2}
}

{"_id":"eCknmZAwb4W7ohnbY","bibbaseid":"malandrino-deprisco-ianulardo-zaccagnino-anadaptivemetaheuristicformusicplagiarismdetectionbasedontextsimilarityandclustering-2022","author_short":["Malandrino, D.","De Prisco, R.","Ianulardo, M.","Zaccagnino, R."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Malandrino"],"firstnames":["Delfina"],"suffixes":[]},{"propositions":[],"lastnames":["De Prisco"],"firstnames":["Roberto"],"suffixes":[]},{"propositions":[],"lastnames":["Ianulardo"],"firstnames":["Mario"],"suffixes":[]},{"propositions":[],"lastnames":["Zaccagnino"],"firstnames":["Rocco"],"suffixes":[]}],"year":"2022","title":"An adaptive meta-heuristic for music plagiarism detection based on text similarity and clustering","abstract":"Plagiarism is a controversial and debated topic in different fields, especially in the Music one, where the commercial market generates a huge amount of money. The lack of objective metrics to decide whether a song is a plagiarism, makes music plagiarism detection a very complex task: often decisions have to be based on subjective argumentations. Automated music analysis methods that identify music similarities can be of help. In this work, we first propose two novel such methods: a text similarity-based method and a clustering-based method. Then, we show how to combine them to get an improved (hybrid) method. The result is a novel adaptive meta-heuristic for music plagiarism detection. To assess the effectiveness of the proposed methods, considered both singularly and in the combined meta-heuristic, we performed tests on a large dataset of ascertained plagiarism and non-plagiarism cases. Results show that the meta-heuristic outperforms existing methods. Finally, we deployed the meta-heuristic into a tool , accessible as a Web application, and assessed the effectiveness, usefulness, and overall user acceptance of the tool by means of a study involving 20 people, divided into two groups, one of which with access to the tool. The study consisted in having people decide which pair of songs, in a predefined set of pairs, should be considered plagiarisms and which not. The study shows that the group supported by our tool successfully identified all plagiarism cases, performing all tasks with no errors. The whole sample agreed about the usefulness of an automatic tool that provides a measure of similarity between two songs.","doi":"10.1007/s10618-022-00835-2","issn":"1384-5810","journal":"Data Mining and Knowledge Discovery","keywords":"Clustering,Evaluation study,Multi-objective optimization,Music plagiarism detection,Text similarity,computational musicology","mendeley-tags":"computational musicology","month":"may","publisher":"Springer US","url":"https://link.springer.com/10.1007/s10618-022-00835-2","bibtex":"@Article{ malandrino.ea2022-adaptive,\n author = {Malandrino, Delfina and {De Prisco}, Roberto and\n Ianulardo, Mario and Zaccagnino, Rocco},\n year = {2022},\n title = {An adaptive meta-heuristic for music plagiarism detection\n based on text similarity and clustering},\n abstract = {Plagiarism is a controversial and debated topic in\n different fields, especially in the Music one, where the\n commercial market generates a huge amount of money. The\n lack of objective metrics to decide whether a song is a\n plagiarism, makes music plagiarism detection a very\n complex task: often decisions have to be based on\n subjective argumentations. Automated music analysis\n methods that identify music similarities can be of help.\n In this work, we first propose two novel such methods: a\n text similarity-based method and a clustering-based\n method. Then, we show how to combine them to get an\n improved (hybrid) method. The result is a novel adaptive\n meta-heuristic for music plagiarism detection. To assess\n the effectiveness of the proposed methods, considered both\n singularly and in the combined meta-heuristic, we\n performed tests on a large dataset of ascertained\n plagiarism and non-plagiarism cases. Results show that the\n meta-heuristic outperforms existing methods. Finally, we\n deployed the meta-heuristic into a tool , accessible as a\n Web application, and assessed the effectiveness,\n usefulness, and overall user acceptance of the tool by\n means of a study involving 20 people, divided into two\n groups, one of which with access to the tool. The study\n consisted in having people decide which pair of songs, in\n a predefined set of pairs, should be considered\n plagiarisms and which not. The study shows that the group\n supported by our tool successfully identified all\n plagiarism cases, performing all tasks with no errors. The\n whole sample agreed about the usefulness of an automatic\n tool that provides a measure of similarity between two\n songs.},\n doi = {10.1007/s10618-022-00835-2},\n issn = {1384-5810},\n journal = {Data Mining and Knowledge Discovery},\n keywords = {Clustering,Evaluation study,Multi-objective\n optimization,Music plagiarism detection,Text\n similarity,computational musicology},\n mendeley-tags= {computational musicology},\n month = {may},\n publisher = {Springer US},\n url = {https://link.springer.com/10.1007/s10618-022-00835-2}\n}\n\n","author_short":["Malandrino, D.","De Prisco, R.","Ianulardo, M.","Zaccagnino, R."],"key":"malandrino.ea2022-adaptive","id":"malandrino.ea2022-adaptive","bibbaseid":"malandrino-deprisco-ianulardo-zaccagnino-anadaptivemetaheuristicformusicplagiarismdetectionbasedontextsimilarityandclustering-2022","role":"author","urls":{"Paper":"https://link.springer.com/10.1007/s10618-022-00835-2"},"keyword":["Clustering","Evaluation study","Multi-objective optimization","Music plagiarism detection","Text similarity","computational musicology"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://hmb.sampaio.me/bibliografia.bib.txt","dataSources":["n6MFY2CscQLDpJ7nT"],"keywords":["clustering","evaluation study","multi-objective optimization","music plagiarism detection","text similarity","computational musicology"],"search_terms":["adaptive","meta","heuristic","music","plagiarism","detection","based","text","similarity","clustering","malandrino","de prisco","ianulardo","zaccagnino"],"title":"An adaptive meta-heuristic for music plagiarism detection based on text similarity and clustering","year":2022}