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A fuzzy model for optical recognition of musical scores

机译：乐谱光学识别的模糊模型

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Optical music recognition aims at reading automatically scanned scores in order to convert them in an electronic format, such as a midi file. We only consider here classical monophonic music: we exclude any music written on several staves, but also any music that contains chords. In order to overcome recognition failures due to the lack of methods dealing with structural information, non-local rules and corrections, we propose a recognition approach integrating structural information in the form of relationships between symbols and of musical rules. Another contribution of this paper is to solve ambiguities by accounting for sources of imprecision and uncertainty, within the fuzzy set and possibility theory framework. We add to a single symbol analysis several rules for checking the consistency of hypotheses: graphical consistency (compatibility between accidental and note, between grace note and note, between note and augmentation dot, etc.), and syntactic consistency (accidentals, tonality, metric). All these rules are combined in order to lead to better decisions. Experimental results on 65 music sheets show that our approach leads to very good results, and is able to correct errors made by other approaches, such as the one of SmartScore.
机译：光学音乐识别的目的是读取自动扫描的乐谱，以便将其转换为电子格式（如midi文件）。在这里，我们仅考虑古典单声道音乐：我们排除了在多个五线谱上编写的任何音乐，但也排除了包含和弦的任何音乐。为了克服由于缺乏处理结构信息，非局部规则和更正的方法而导致的识别失败，我们提出了一种以符号和音乐规则之间的关系形式集成结构信息的识别方法。本文的另一个贡献是通过在模糊集和可能性理论框架内考虑不精确性和不确定性的来源来解决歧义。我们在单个符号分析中添加了一些规则来检查假设的一致性：图形一致性（偶然性和音符之间的兼容性，宽限度音符和音符之间的兼容性，音符和扩充点之间的兼容性等）以及句法一致性（偶然性，音调，度量） ）。所有这些规则结合在一起，以便做出更好的决策。在65张乐谱上的实验结果表明，我们的方法可产生非常好的效果，并且能够纠正其他方法（例如SmartScore之一）造成的错误。

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