论文标题
HouseX：一个细粒度的House音乐数据集及其在音乐行业的潜力
HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry
论文作者
论文摘要
机器声音分类一直是音乐技术的基本任务之一。声音分类的主要分支是音乐流派的分类。但是，尽管涵盖了大多数音乐流派，但现有的音乐流派数据集通常不包含指示详细的音乐子类别的细颗粒标签。考虑到混音带或DJ（现场）中的歌曲流派的一致性，我们已经收集并注释了一个家庭音乐数据集，该数据集提供了4个子类别标签，即未来的House，Bass House，Progressive House，Progressive House和Melodic House。实验表明，我们的注释很好地表现出不同类别的特征。此外，我们还建立了基线模型，该模型根据轨道的MEL光谱图对子类别进行了分类，从而取得了竞争激烈的结果。此外，我们已经提出了一些数据集和基线模型的应用程序方案，并使用模拟的科幻隧道作为一个简短的演示式构建和渲染的3D建模软件，并具有由我们模型的输出自动化的灯的颜色。
Machine sound classification has been one of the fundamental tasks of music technology. A major branch of sound classification is the classification of music genres. However, though covering most genres of music, existing music genre datasets often do not contain fine-grained labels that indicate the detailed sub-genres of music. In consideration of the consistency of genres of songs in a mixtape or in a DJ (live) set, we have collected and annotated a dataset of house music that provide 4 sub-genre labels, namely future house, bass house, progressive house and melodic house. Experiments show that our annotations well exhibit the characteristics of different categories. Also, we have built baseline models that classify the sub-genre based on the mel-spectrograms of a track, achieving strongly competitive results. Besides, we have put forward a few application scenarios of our dataset and baseline model, with a simulated sci-fi tunnel as a short demo built and rendered in a 3D modeling software, with the colors of the lights automated by the output of our model.
