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The problem of automatic audio to score alignment is nowadays well
\nunderstood, leading to high accuracies even for polyphonic music signals with
\nseveral instruments playing at the same time. Traditionally, the evaluation
\nmetrics rely on the distance between the ground truth and the estimated
\nnote onsets, considering a fixed tolerance threshold (e.g. 200 ms). This
\ncriterion is suitable for many applications such as page turning or informed
\nsound source separation. However, other applications as automatic musical
\naccompaniment require more advance alignment, considering musical aspects
\nsuch as tempo in order to control the time differences between onsets of
\nconsecutive notes, leading to aligned scores with more musical meaning.
\nThe aim of this master thesis is to provide a solution to guide the align-
\nment process from a more musical point of view in order to implement an
\nautomatic musical accompaniment system. To this end, the most reliable
\nscore follower algorithm of the MIREX competition is used as a base line
\nand extended taking into account the musical restrictions. The proposed
\nmethod is based on Dynamic Time Warping (DTW), where a tempo con-
\ntroller is enforced within the minimum cost path computation in a novel
\nway.
Master thesis presented and approved by the Master in Sound and Music Computing of the Universitat Pompeu Fabra, Barcelona.
\r\n", "description": "Thesis of the Master in Sound and Music Computing (https://www.upf.edu/web/smc) of the Universitat Pompeu Fabra, Barcelona (https://www.upf.edu)", "page": "", "title": "Master in Sound and Music Computing" }, "revision_id": 0, "slug": "smc-master", "updated": "2020-04-28T17:00:02.532998+00:00" } ], "ids": [ "78c30cbc-1c1b-4ac2-a20b-7715387167e0" ] }, "id": "1117838", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.1117838", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.1117839", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:1117839", "provider": "oai" } }, "revision_id": 7, "stats": { "all_versions": { "data_volume": 327428640.0, "downloads": 168, "unique_downloads": 162, "unique_views": 158, "views": 204 }, "this_version": { "data_volume": 327428640.0, "downloads": 168, "unique_downloads": 162, "unique_views": 158, "views": 204 } }, "status": "published", "updated": "2020-01-20T17:04:17.196668+00:00", "versions": { "index": 1, "is_latest": true } }