用小波滤波分析民歌旋律，提升曲调分类准确率
Wavelet-Filtering of Symbolic Music Representations for Folk Tune Segmentation and Classification
- 通过哈尔小波变换在特定尺度滤波旋律信号
- 交叉验证显示分类准确率优于传统方法
- 适合音乐信息检索与民族音乐分析研究者
本研究评估一种基于小波滤波的机器学习方法，用于对民间歌曲的符号化旋律进行分段与曲调分类。旋律以离散的音高-时间信号表示，采用连续小波变换（CWT）结合哈尔小波，在特定尺度下提取强调时序特征的滤波版本。利用小波系数局部极大值作为分段边界，基于欧氏距离和曼哈顿距离的k近邻算法对片段分类，并与基于格式塔理论的方法及直接在音高信号上应用的指标进行比较。实验结果表明，在优化时间尺度及其他参数后，小波基分段与滤波方法在交叉验证中实现了更高分类准确率。
原文摘要 · Abstract (English)
The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet filtering. The method is compared with previously proposed Gestalt-based method. Melodies are represented as discrete symbolic pitch-time signals. We apply the continuous wavelet transform (CWT) with the Haar wavelet at specific scales, obtaining filtered versions of melodies emphasizing their information at particular time-scales. We use the filtered signal for representation and segmentation, using the wavelet coefficients' local maxima to indicate local boundaries and classify segments by means of k-nearest neighbours based on standard vector-metrics (Euclidean, cityblock), and compare the results to a Gestalt-based segmentation method and metrics applied directly to the pitch signal. We found that the wavelet based segmentation and wavelet-filtering of the pitch signal lead to better classification accuracy in cross-validated evaluation when the time-scale and other parameters are optimized.
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