With the rapid development of information technology and communication, digital music has grown and exploded. Regarding how to quickly and accurately retrieve the music that users want from huge bulk of music repository, music feature extraction and classification are considered as an important part of music information retrieval and have become a research hotspot in recent years. Traditional music classification approaches use a large number of artificially designed acoustic features. The design of features requires knowledge and in-depth understanding in the domain of music. The features of different classification tasks are often not universal and comprehensive. The existing approach has two shortcomings as follows: ensuring the validity and accuracy of features by manually extracting features and the traditional machine learning classification approaches not performing well on multiclassification problems and not having the ability to be trained on large-scale data. Therefore, this paper converts the audio signal of music into a sound spectrum as a unified representation, avoiding the problem of manual feature selection. According to the characteristics of the sound spectrum, the research has combined 1D convolution, gating mechanism, residual connection, and attention mechanism and proposed a music feature extraction and classification model based on convolutional neural network, which can extract more relevant sound spectrum characteristics of the music category. Finally, this paper designs comparison and ablation experiments. The experimental results show that this approach is better than traditional manual models and machine learning-based approaches.

中文翻译：

---

基于深度学习的音乐特征提取与分类算法

随着信息技术和通信的飞速发展，数字音乐不断发展壮大。关于如何快速，准确地从庞大的音乐库中检索用户想要的音乐，音乐特征的提取和分类被认为是音乐信息检索的重要组成部分，并且已经成为近年来的研究热点。传统的音乐分类方法使用了大量的人工设计的声学特征。功能的设计需要对音乐领域的知识和深入的了解。不同分类任务的功能通常不具有普遍性和全面性。现有方法有以下两个缺点：通过手动提取特征和传统的机器学习分类方法来确保特征的有效性和准确性，传统的机器学习分类方法在多分类问题上表现不佳，也无法在大规模数据上进行训练。因此，本文将音乐的音频信号转换成声谱作为统一表示，避免了手动特征选择的问题。根据声谱的特点，结合一维卷积，门控机制，残差连接和注意力机制，提出了基于卷积神经网络的音乐特征提取和分类模型，可以提取出更相关的声谱特征。音乐类别。最后，本文设计了比较和消融实验。