The Use of Large Corpora to Train a New Type of Key-Finding Algorithm: An Improved Treatment of the Minor Mode
The Use of Large Corpora to Train a New Type of Key-Finding Algorithm: An Improved Treatment of the Minor Mode
复制标题
利用大型语料库训练新型找键算法：次要模式的改进处理
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
D. Shanahan
中科院分区：
文献类型：
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作者：
Joshua Albrecht;D. Shanahan
关键估计的计算模型一直在努力模仿人类听众的准确性水平，特别是在小调模式下。目前的研究提出了一种新的关键发现算法，它利用欧氏距离，而不是相关性，并训练了一个大的音乐样本的统计特性。一个模型是在一个490个片段的数据集上训练的，这些片段被编码成Humbrum“克恩”格式，其中密钥是已知的。该模型在492件的储备数据集上进行了测试，发现其总体准确性明显高于以前的模型。此外，我们确定了单独的准确性评级的主要模式和次要模式的工作，现有的关键发现模型和报告，大多数现有的模型提供更大的准确性，而不是次要模式的工作。所提出的密钥查找算法在次要模式作品上的表现比所有其他测试模型更准确，尽管它并没有比Aarden（2003），Bellman（2005）或Sapp（2011）创建的模型更好。最后，结合Aarden-Essen模型（2003）和所提出的算法的算法，并提出了一个更准确的关键评估比所有其他现存的模型。
Computational models of key estimation have struggled to emulate the accuracy levels of human listeners, especially with pieces in the minor mode. The current study proposes a new key-finding algorithm, which utilizes Euclidean distance, rather than correlation, and is trained on the statistical properties of a large musical sample. A model was trained on a dataset of 490 pieces encoded into the Humdrum “kern” format, in which the key was known. This model was tested on a reserve dataset of 492 pieces, and was found to have a significantly higher overall accuracy than previous models. In addition, we determined separate accuracy ratings for major mode and minor mode works for the existing key-finding models and report that most existing models provide greater accuracy for major mode rather than minor mode works. The proposed key-finding algorithm performs more accurately on minor mode works than all of the other models tested, although it does not perform significantly better than the models created by Aarden (2003), Bellman (2005), or Sapp (2011). Finally, an algorithm that combines the Aarden-Essen model (2003) and the proposed algorithm is suggested, and results in significantly more accurate key assessments than all of the other extant models.