Music is an indispensable part of our life and study and is one of the most important forms of multimedia applications. With the development of deep learning and neural network in recent years, how to use cutting-edge technology to study and apply music has become a research hotspot. Music waveform is not only the main form of music frequency but also the basis of music feature extraction. This paper first designs a method of note extraction based on the fast Fourier transform principle of the audio signal packet route under the self-organizing map (SOM neural network) which can accurately extract the musical features of the note, such as amplitude, loudness, period, and so on. Secondly, the audio segments are divided into summary by adding window moving matching method, and the music features such as amplitude, loudness, and period of each bar are obtained according to the performance of audio signal in each bar. Finally, according to the similarity of the audio music theory of the adjacent summary of each bar, the audio segments are divided, and the music features of each segment are obtained. The traditional recurrent neural network (RNN) is improved, and the SOM neural network is used to recognize the audio emotion features. The final experimental results show that the proposed method based on SOM neural network and big data can effectively extract and analyze music waveform features. Compared with previous studies, this paper creatively proposed a new algorithm, which can more accurately and quickly extract and analyze the data sound waveform, and used SOM neural network to analyze the emotion model contained in music for the first time.

中文翻译：

---

基于SOM神经网络和大数据的音乐波形分析

音乐是我们生活和学习中不可或缺的一部分，是多媒体应用最重要的形式之一。近年来随着深度学习和神经网络的发展，如何利用前沿技术来研究和应用音乐已成为研究热点。音乐波形不仅是音乐频率的主要形式，也是音乐特征提取的基础。本文首先设计了一种基于自组织图（SOM神经网络）下音频信号包路径的快速傅里叶变换原理的音符提取方法，可以准确地提取音符的音乐特征，如振幅、响度、期等。其次，通过加入移窗匹配的方法对音频片段进行摘要，并根据每个小节音频信号的表现获得每个小节的幅度、响度、周期等音乐特征。最后，根据每个小节相邻摘要的音频乐理相似度，划分音频片段，得到每个片段的音乐特征。对传统的循环神经网络（RNN）进行改进，采用SOM神经网络来识别音频情感特征。最终的实验结果表明，所提出的基于SOM神经网络和大数据的方法能够有效地提取和分析音乐波形特征。与以往的研究相比，本文创造性地提出了一种新的算法，能够更加准确、快速地提取和分析数据声音波形，并首次使用SOM神经网络来分析音乐中包含的情感模型。