• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of signal processing systems for signal, image, and video technology >PureMIC: A New Audio Dataset for the Classification of Musical Instruments based on Convolutional Neural Networks
【24h】

PureMIC: A New Audio Dataset for the Classification of Musical Instruments based on Convolutional Neural Networks

机译：威胁：基于卷积神经网络的乐器分类的新音频数据集

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automatic classification of musical instruments from audio relies heavily on datasets of acoustic recordings of the instruments to train models of those instruments. To do this, precise labels of the instrument's events are mandatory. Also, it is very difficult to obtain such labels, especially in polyphonic performances. OpenMic-2018 is a polyphonic dataset created specifically with the aim to train instrument models. However, this dataset is based on weak and incomplete labels. The automatic classification of sound events, based on the VGGish bottleneck layer as proposed before by the AudioSet, implies the classification of only one second at a time, making it hard to find the label of that exact moment. To answer this question, this paper proposes PureMIC, a new strongly labeled dataset (SLD) that isolates 1000 single instrument clips manually labeled. Moreover, the proposed model classifies clips over time and also enhances the labeling robustness of a high number of unlabeled samples in OpenMIC-2018 due to its ability of classification over time. In the paper we disambiguate and report the automatic labeling of previously unlabeled samples. The proposed new labels achieve a mean average precision (mAP) of 0.701 for OpenMIC test data, outperforming its baseline (0.66). The code is released online so that the research community can replicate and follow the proposed implementation.
机译：来自音频的乐器自动分类严重依赖于仪器的声学记录数据集，以培训这些仪器的模型。为此，仪器事件的精确标签是强制性的。而且，非常难以获得这种标签，尤其是在复音性能中。 OpenMic-2018是一个专门创建的Polyphonic数据集，其目的是培训仪器模型。但是，此数据集基于弱和不完整的标签。基于Audioset之前提出的VAGATH瓶颈层的声音事件的自动分类意味着一次只有一秒钟的分类，使得很难找到该确切时刻的标签。为了回答这个问题，本文提出了一种诸如手动标记的1000个单个仪器夹的新强大标记的数据集（SLD）。此外，所提出的模型随着时间的推移来对剪辑进行分类，并且由于其随时间的分类能力，增强了OpenMic-2018中的大量未标记样本的标记稳健性。在论文中，我们消除并报告先前未标记的样本的自动标记。所提出的新标签实现了OpenMic测试数据的平均平均精度（MAP）为0.701，优于其基线（0.66）。该代码在线发布，以便研究社区可以复制和遵循拟议的实施。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号