{"content":[{"text":"Benchmarking Wave-to-MIDI Transcription Tools \n Henrique B. S. Leão, Germano F. Guimarães, Geber L. Ramalho \n and Sérgio V. Cavalcante \n Centro de Informática \n Universidade Federal de Pernambuco \n Cx Postal 7851 CEP 50732-970 Recife-PE Brazil \n {hbsl,gfg,glr,svc}@cin.ufpe.br","bbox":{"left":0.215395,"top":0.155701,"width":0.5713280000000001,"height":0.12633399999999997}},{"text":"Abstract","bbox":{"left":0.458622,"top":0.29598,"width":0.08497600000000005,"height":0.022286000000000028}},{"text":"This paper describes a methodology for building a benchmark to evaluate automatic music \n transcription tools, i.e., softwares that can extract music information from digital audio signals. \n There are numerous applications to these tools, like data acquisition for music virtual libraries \n and computer-aided music learning for instance. The main purpose of this benchmark is to \n present a method to ranking the current tools, as well as the characterisation of their mistakes and \n the respective causes. We expect that the results will be valuable both to users, guiding their \n choice for the most suitable tool according to their needs, and to tools’ developers, showing any \n essential improvements required.","bbox":{"left":0.116571,"top":0.329811,"width":0.769672,"height":0.13052399999999997}},{"text":"1 Introduction \n An important issue in current computer music research is automatic music transcription, i.e., the \n extraction of note information such as pitch, duration and intensity from digital audio signals \n [10]. Such a transcription may be applied for several purposes, including data acquisition for \n music virtual libraries, automatic music accompaniment systems, computer-aided music learning, \n etc. \n Motivated by our interests in music accompaniment systems [11], we started a project \n whose initial goal was to implement a transcription tool by improving one of the existing \n algorithms. However, after some research on the subject, it was noticed that there is no \n methodology or tool either to validate/compare the results of these algorithms, or to characterise \n the errors. \n The solution to this lack of formal validation framework can be achieved by the use of \n benchmark techniques [12]. A benchmark is defined as a set of tests aiming either at measuring a \n system performance with respect to given tasks, or at comparing systems that use different \n technologies for the same task. A benchmark can also be used as monitoring and diagnosis tools, \n since it provides data to discover and correct common errors as well as to assess the impact of \n any important change implemented in the system under development. \n This article describes a methodology for building a benchmark to evaluate automatic \n music transcription tools. The main goal of this benchmark is to provide a ranking of the current \n tools, as well as the characterisation of their mistakes and the respective causes. We hope that the \n results will be useful both to users, guiding their choice for the most appropriate tool according to \n their needs, and to tools’ developers, showing any critical improvements needed. \n Section 2 shows the conceptual project of the benchmark step-by-step. Section 3 discusses \n some implementation details and initial results. The results are not conclusive, since the project is","bbox":{"left":0.116571,"top":0.471847,"width":0.7701,"height":0.3912439999999999}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}