A data set of recorded single played tones of a concert grand piano is investigated using Machine Learning (ML) on psychoacoustic timbre features. The examined instrument has been recorded at two stages: firstly right after manufacture and secondly after being played in a concert hall for one year. A previous study [Plath2019] revealed that listeners clearly distinguished both stages but no clear correlation with acoustics, signal processing tools or verbalizations of perceived differences could be found. Using a Self-Organizing Map (SOM), training single as well as double feature sets, it can be shown that spectral flux is able to perfectly cluster the two stages. Sound Pressure Level (SPL), roughness, and fractal correlation dimension (as a measure for initial transient chaoticity) are furthermore able to order the keys with respect to high and low notes. Combining spectral flux with the three other features in double-feature training sets maintains stage clustering only for SPL and fractal dimension, showing sub-clusters for both stages. These sub-clusters point to a homogenization of SPL for stage 2 with respect to stage 1 and a pronounced ordering and sub-clustering of key regions with respect to initial transient chaoticity.
翻译:使用机器学习(ML),对音乐大钢琴的单曲调子进行了调查。经过检查的仪器分两个阶段记录:制造后第一,制造后第二,在音乐厅播放一年后第二。先前的一项研究[Plath2019]显示,听众明显区分了两个阶段,但与声学、信号处理工具或认知差异的言辞没有明显关联。使用自组织地图(SOM)、培训单集和双功能集,可以显示光谱通量能够完美地将两个阶段组合在一起。声压等级(SPL)、粗糙度和分光谱相关维度(作为初始易变混乱度的一种衡量标准)还可以为高低音调订下键。将光谱通量与双功能培训组中的其他三个特征相结合,只维持SPL和分集层的阶段组合,显示两个阶段的分集。这些子集显示SPL第2阶段的同质化点,与第1阶段和明确定序和分集层区域相邻。