Performance Error Detection and Post-Processing for Fast and Accurate Symbolic Music Alignment
Performance Error Detection and Post-Processing for Fast and Accurate Symbolic Music Alignment
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发表时间：
2017
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通讯作者：
Eita Nakamura;Kazuyoshi Yoshii;H. Katayose
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作者：
Eita Nakamura;Kazuyoshi Yoshii;H. Katayose
本文提出了一种快速、准确的复调符号音乐信号配准方法。众所周知，为了精确地对准钢琴演奏，需要使用声音结构的方法。然而，这样的方法通常具有高计算成本，并且它们仅在给出先前语音信息时才适用。指出了对准误差通常伴随着对准信号中的性能误差。这表明了通过快速（但不那么准确）对准方法来校正（或重新对准）初步结果的可能性，其中精细方法应用于对准信号的有限段，以节省计算成本。为了实现这一点，我们开发了一种方法，用于检测性能错误和重新排列的方法，工作快速，准确地在局部区域周围的性能错误。为了消除对先前语音信息的依赖，对局部区域中的参考信号执行语音分离。通过将我们的方法应用于先前提出的隐马尔可夫模型所获得的结果，以较短的计算时间实现了最高的精度。我们的源代码发布在随附的网页中，以及用于检查和更正对齐结果的用户界面。
This paper presents a fast and accurate alignment method for polyphonic symbolic music signals. It is known that to accurately align piano performances, methods using the voice structure are needed. However, such methods typically have high computational cost and they are applicable only when prior voice information is given. It is pointed out that alignment errors are typically accompanied by performance errors in the aligned signal. This suggests the possibility of correcting (or realigning) preliminary results by a fast (but not-so-accurate) alignment method with a refined method applied to limited segments of aligned signals, to save the computational cost. To realise this, we develop a method for detecting performance errors and a realignment method that works fast and accurately in local regions around performance errors. To remove the dependence on prior voice information, voice separation is performed to the reference signal in the local regions. By applying our method to results obtained by previously proposed hidden Markov models, the highest accuracies are achieved with short computation time. Our source code is published in the accompanying web page, together with a user interface to examine and correct alignment results.