This paper presents a web application that retrieves songs from YouTube and classifies them into music genres. The tool explained in this study is based on models trained using the musical collection data from Audioset. For this purpose, we have used classifiers from distinct Machine Learning paradigms: Probabilistic Graphical Models (Naive Bayes), Feed-forward and Recurrent Neural Networks and Support Vector Machines (SVMs). All these models were trained in a multi-label classification scenario. Because genres may vary along a song's timeline, we perform classification in chunks of ten seconds. This capability is enabled by Audioset, which offers 10-second samples. The visualization output presents this temporal information in real time, synced with the music video being played, presenting classification results in stacked area charts, where scores for the top-10 labels obtained per chunk are shown. We briefly explain the theoretical and scientific basis of the problem and the proposed classifiers. Subsequently, we show how the application works in practice, using three distinct songs as cases of study, which are then analyzed and compared with online categorizations to discuss models performance and music genre classification challenges.

中文翻译：

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针对偏瘫儿童的家庭功能性电刺激辅助手部治疗视频游戏：开发和概念验证


本文介绍了一个从 YouTube 检索歌曲并将其分类为音乐流派的 Web 应用程序。本研究中解释的工具基于使用 Audioset 的音乐收藏数据训练的模型。为此，我们使用了来自不同机器学习范式的分类器：概率图形模型（朴素贝叶斯）、前馈和循环神经网络以及支持向量机 (SVM)。所有这些模型都在多标签分类场景中进行训练。由于流派可能会随着歌曲的时间线而变化，因此我们以十秒为单位进行分类。此功能由 Audioset 启用，它提供 10 秒的样本。可视化输出实时呈现这些时间信息，与正在播放的音乐视频同步，在堆叠面积图中呈现分类结果，其中显示每个块获得的前 10 个标签的分数。我们简要解释该问题的理论和科学基础以及所提出的分类器。随后，我们展示了该应用程序在实践中的工作原理，使用三首不同的歌曲作为研究案例，然后对它们进行分析并与在线分类进行比较，以讨论模型性能和音乐流派分类挑战。