Music genre classification using convolution temporal pooling network

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Vijayameenakshi T. M, Swapna T. R
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引用次数: 0

Abstract

Music genre classification is one of the most interesting topics in digital music. Classifying genres is basically subjective, and different listeners may perceive genres in various ways. Furthermore, it might be difficult to classify some songs accurately since they belong to numerous genres. Genres are incredibly wide and ill-defined categories, which makes them problematic. Thus, genre-based measures are inherently inaccurate and coarse. Moreover, not every piece of music cleanly fits into a particular genre. Many papers based on deep neural networks perform sound recognition and classification with input images of audio, which do not affect the time–frequency representation of a signal. The traditional method adds waveform augmentation to the audio signal, thereby increasing the network's training speed. This paper considers music genre classification with the convolution temporal pooling framework and explores the impact of adding the SpecAugment method to augment the spectrogram itself. The augmented spectrogram is then fed into a convolutional temporal pooling network. In this model, the temporal and pooling layers identify the genre pattern and classify the songs based on the genre. It also predicts these duplication that will occur in the given sample. We apply this model to the GTZAN dataset, a widely used dataset for music genre classification. This method improves the identification of Rock and Pop song and also eliminates the replication of the songs. The trained model reports an accuracy of 0.75 for training a 30-s audio file.

Abstract Image

利用卷积时空池网络进行音乐流派分类
音乐流派分类是数字音乐领域最有趣的话题之一。流派分类基本上是主观的,不同的听众可能会以不同的方式感知流派。此外,有些歌曲可能很难准确分类,因为它们属于多种流派。流派是一个非常宽泛且定义不清的类别,这就给流派分类带来了问题。因此,基于流派的测量方法本质上是不准确和粗糙的。此外,并非每首音乐都能准确地归入某一特定流派。许多基于深度神经网络的论文都是通过输入音频图像来进行声音识别和分类的,这不会影响信号的时频表示。传统方法会对音频信号进行波形增强,从而提高网络的训练速度。本文考虑了使用卷积时空池框架进行音乐流派分类的问题,并探讨了添加 SpecAugment 方法对增强频谱图本身的影响。然后将增强频谱图输入卷积时序池网络。在该模型中,时序层和池化层可识别流派模式,并根据流派对歌曲进行分类。它还能预测给定样本中会出现的重复现象。我们将该模型应用于 GTZAN 数据集,这是一个广泛用于音乐流派分类的数据集。这种方法提高了对摇滚和流行歌曲的识别率,并消除了歌曲的重复现象。经过训练的模型在训练 30 秒音频文件时的准确率为 0.75。
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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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