论文标题
通过拓扑数据分析和人工神经网络的机器组成
Machine Composition of Korean Music via Topological Data Analysis and Artificial Neural Network
论文作者
论文摘要
基于人工神经网络的常见AI音乐组成算法是通过喂食大量音乐作品并创建可以产生类似于输入音乐数据的音乐的人工神经网络来训练机器。这种方法是黑框优化，即，基本的构图算法通常是用户不知道的。 在本文中，我们提出了一种机器构图的方式，该方法训练机器嵌入在给定音乐数据中的组合原理，而不是直接喂养音乐作品。我们通过使用{\ color {black} {重叠}}矩阵提出的\ cite {tpj}提出的矩阵来提出这种方法。在\ cite {tpj}中，一种使用拓扑数据分析（TDA）进行了分析，尤其是使用持续的同源性，已经分析了一种韩国音乐，例如SuyeonJangjigok，例如SuyeonJangjigok。由于原始音乐数据不适合TDA分析，因此首先将音乐数据重建为图形。图的节点定义为由每个音乐音符的音高和持续时间组成的二维矢量。当这些节点在音乐流中连续出现时，创建两个节点之间的边缘。根据此类外观的频率定义距离。通过构造图上的TDA，为给定的音乐找到了一组独特的周期。在\ cite {tpj}中，已经提出了{\ it {\ color {black} {重叠}}矩阵}的新概念，该矩阵}}}}}}}}}}}，它以矩阵形式可视化这些周期如何通过音乐流进行互连。 在本文中，我们解释了如何使用{\ color {black {black} {重叠}}矩阵用于机器组成。 {\ color {black} {叠加}}矩阵使得可以以新的音乐作品算法为单位，并为所需的人工神经网络提供种子音乐。在本文中，我们使用{\ it dodeuri}音乐并解释详细的步骤。
Common AI music composition algorithms based on artificial neural networks are to train a machine by feeding a large number of music pieces and create artificial neural networks that can produce music similar to the input music data. This approach is a blackbox optimization, that is, the underlying composition algorithm is, in general, not known to users. In this paper, we present a way of machine composition that trains a machine the composition principle embedded in the given music data instead of directly feeding music pieces. We propose this approach by using the concept of {\color{black}{Overlap}} matrix proposed in \cite{TPJ}. In \cite{TPJ}, a type of Korean music, so-called the {\it Dodeuri} music such as Suyeonjangjigok has been analyzed using topological data analysis (TDA), particularly using persistent homology. As the raw music data is not suitable for TDA analysis, the music data is first reconstructed as a graph. The node of the graph is defined as a two-dimensional vector composed of the pitch and duration of each music note. The edge between two nodes is created when those nodes appear consecutively in the music flow. Distance is defined based on the frequency of such appearances. Through TDA on the constructed graph, a unique set of cycles is found for the given music. In \cite{TPJ}, the new concept of the {\it {\color{black}{Overlap}} matrix} has been proposed, which visualizes how those cycles are interconnected over the music flow, in a matrix form. In this paper, we explain how we use the {\color{black}{Overlap}} matrix for machine composition. The {\color{black}{Overlap}} matrix makes it possible to compose a new music piece algorithmically and also provide a seed music towards the desired artificial neural network. In this paper, we use the {\it Dodeuri} music and explain detailed steps.
