Carnatic music (CM) is characterized by continuous pitch variations called gamakas, which are learned by example. Precision is measured on the points of zero-slope in gamaka- and non-gamaka-segments of the pitch curve as the standard deviation (SD) of the error in their pitch with respect to targets. Two previous techniques are considered to identify targets: the nearest semitone and the most likely mean of a semi-continuous Gaussian mixture model. These targets are employed irrespective of where the points of zero-slope occur in the pitch curve. The authors propose segmenting CM pitch curves into non-overlapping components called constant-pitch notes (CPNs) and stationary points (STAs), i.e., points where the pitch curve outside the CPNs changes direction. Targets are obtained statistically from the histograms of the mean pitch-values of CPNs, anchors (CPNs adjacent to STAs), and STAs. The upper and lower quartiles of SDs of errors in long CPNs (9–15 cents), short CPNs (20–26 cents), and STAs (41–54 cents) are separable, which justifies the component-wise treatment. The CPN-STA model also brings out a hitherto unreported structure in rāgas and explains the precision obtained using the previous techniques.

中文翻译：

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数据驱动的对Carnatic音乐中音高曲线各部分的精度的测量。

carnatic音乐（CM）的特征是连续音高变化，称为gamaka s，这是通过示例学习的。在gamaka和非gamaka的零坡度点上测量精度-音高曲线的段，作为音高相对于目标的误差的标准偏差（SD）。考虑使用两种先前的技术来识别目标：半连续高斯混合模型的最近半音和最可能的均值。不管音高曲线中零斜率的点出现在哪里，都采用这些目标。作者建议将CM音高曲线分段为不重叠的分量，称为恒定音高音符（CPN）和固定点（STA），即CPN外部音高曲线改变方向的点。从CPN，锚点（与STA相邻的CPN）和STA的平均音高值的直方图中统计地获取目标。长CPN（9-15美分），短CPN（20-26美分）和STA（41-54美分）的错误SD的上下四分位数是可分离的，这证明了按组件进行处理是合理的。CPN-STA模型还提出了迄今为止尚未报道的结构RAGA S和解释了使用以前的技术获得的精度。