用哈尔小波滤波旋律信号，实现更精准的旋律分割与分类。
An approach to melodic segmentation and classification based on filtering with the Haar-wavelet
- 通过哈尔小波对音高信号做单尺度滤波，提取旋律结构特征。
- 在巴赫二部创意曲中，分类准确率优于未滤波和格式塔分割法。
- 适合音乐结构分析，尤其适用于符号化旋律数据的自动处理。
我们提出一种基于哈尔小波滤波的旋律分割与分类新方法。该方法将音高视为随时间变化的信号，利用连续哈尔小波变换提取单尺度信号 w_s，通过局部极大值或零交叉点进行旋律分割，并采用 k 近邻算法结合欧氏距离与曼哈顿距离对片段进行分类。在两个任务上评估：一是从巴赫《二部创意曲》（BWV 772-786）中识别旋律片段的原作，表现优于未滤波音高信号与格式塔分割法；二是对 360 首荷兰民谣按 26 个曲调家族分类，性能与纯音高信号相当，但不及多特征字符串匹配方法。
原文摘要 · Abstract (English)
We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wavelet, and we evaluate it on two tasks. The filtered signal corresponds to a single-scale signal ws from the continuous Haar wavelet transform. The melodies are first segmented using local maxima or zero-crossings of w_s. The segments of w_s are then classified using the k-nearest neighbour algorithm with Euclidian and city-block distances. The method proves more effective than using unfiltered pitch signals and Gestalt-based segmentation when used to recognize the parent works of segments from Bach's Two-Part Inventions (BWV 772-786). When used to classify 360 Dutch folk tunes into 26 tune families, the performance of the method is comparable to the use of pitch signals, but not as good as that of string-matching methods based on multiple features.
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