基于发声的鼓样本检索的深度条件表示学习
Deep Conditional Representation Learning for Drum Sample Retrieval by Vocalisation
摘要：
用人声模仿乐器是音乐制作人之间交流思想的有效方式，从勾勒旋律线到阐明所需的声音。出于这个原因，人们对构建应用程序越来越感兴趣，这些应用程序允许艺术家仅通过声音模仿就可以从大型声音库中有效地挑选目标样本。在这项研究中，我们研究了条件自动编码器模型的潜力，学习鼓样本检索发声（DSRV）的信息功能。我们使用四个评估指标评估了它们的嵌入的有用性，其中两个相对于它们的声学特性，其中两个相对于它们的感知特性，通过人类听众的相似性评级。结果表明，以声音类型标签（鼓与模仿）和鼓型标签（踢与圈套与封闭的踩帽与开放的踩帽）为条件的模型学习DSRV的信息嵌入最丰富。最后，我们通过Mantel测试研究了声音模仿风格的个体差异，发现参与者之间存在显著差异，突出了设计DSRV系统时用户信息的重要性。
英文摘要：
Imitating musical instruments with the human voice is an efficient way of communicating ideas between music producers, from sketching melody lines to clarifying desired sonorities. For this reason, there is an increasing interest in building applications that allow artists to efficiently pick target samples from big sound libraries just by imitating them vocally. In this study, we investigated the potential of conditional autoencoder models to learn informative features for Drum Sample Retrieval by Vocalisation (DSRV). We assessed the usefulness of their embeddings using four evaluation metrics, two of them relative to their acoustic properties and two of them relative to their perceptual properties via human listeners' similarity ratings. Results suggest that models conditioned on both sound-type labels (drum vs imitation) and drum-type labels (kick vs snare vs closed hi-hat vs opened hi-hat) learn the most informative embeddings for DSRV. We finally looked into individual differences in vocal imitation style via the Mantel test and found salient differences among participants, highlighting the importance of user information when designing DSRV systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
