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首页> 外文会议>IIAI International Congress on Advanced Applied Informatics >Improving Pitch Class Profile for Musical Chords Recognition Combining Major Chord Filters and Convolution Neural Networks
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Improving Pitch Class Profile for Musical Chords Recognition Combining Major Chord Filters and Convolution Neural Networks

机译：结合主要和弦滤波器和卷积神经网络，改善和弦识别的音调类配置文件

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In this paper, we consider the challenging problem of music recognition and present an effective deep learning based method using a convolution neural network for chord recognition. It has known that a pitch class profile (PCP) is the commonly signal representation of musical harmonic analysis. However, the PCP vector is not expressive enough for chord recognition, which often occurs in many real-world environments. In this study, we extend the PCP vector scheme to address the limitation. Our proposed method basically consists of two major steps. First, we introduce novel filters and apply then to PCP vector to transform the vector into membership of 7 major chords as features to represent the input matrix. The second step is to efficiently learning feature on the transformed matrix (2D-PCP) using convolution neural network. We propose a trainable, data-driven approach that automatically learns features and its classifier simultaneously. Experimental results conducted on the task of musical chords recognition that the proposed method achieves improvements of classification accuracy more than 40% in accuracy in comparing with based line methods.
机译：在本文中，我们考虑了音乐识别的挑战性问题，并提出了一种使用卷积神经网络进行和弦识别的有效的基于深度学习的方法。众所周知，音高等级简档（PCP）是音乐谐波分析的常用信号表示。但是，PCP矢量的表达能力不足以识别和弦，而这通常发生在许多现实环境中。在这项研究中，我们扩展了PCP向量方案以解决该限制。我们提出的方法基本上包括两个主要步骤。首先，我们介绍新颖的滤波器，然后将其应用于PCP矢量，以将矢量转换为7个主要和弦的隶属关系，以表示输入矩阵。第二步是使用卷积神经网络有效地学习变换后的矩阵（2D-PCP）上的特征。我们提出了一种可训练的，数据驱动的方法，该方法可以自动同时自动学习功能及其分类器。针对和弦识别任务进行的实验结果表明，与基于线性方法的分类方法相比，该方法可将分类精度提高40％以上。

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