• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Intelligent Technologies >A Novel Convolutional Neural Network Model for Musical Instruments’ Classification: A Deep Signal Processing Approach
【24h】

A Novel Convolutional Neural Network Model for Musical Instruments’ Classification: A Deep Signal Processing Approach

机译：用于乐器分类的新型卷积神经网络模型：深度信号处理方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Stress management is a challenge in this modern world, and several methods are being practiced such as diet, exercise, sleep, meditation and relaxation. One of the relaxation methods is the Music Therapy, where one listens to Vocal music, Instrumental music, Hindustani classical music, and Western music etc. In instrument music, various types of instruments are used and these instruments vary from country to country, which produce different sounds. Based on the sounds, humans recognize these instruments with certain efficacy that varies with the expertise. In this paper, we propose the classification of 14 categories of musical instruments, namely, bells, cello, clarinet, crotales, double Bass, flute, piano, saxophone, trombone, trumpet, vibraphone, viola, violin, and xylophone etc. based on the sound signal. A total of 8,365 spectrograms, across 14 classes, are used. We have deployed five pre-trained CNN models such as Efficientnet, Googlenet, ResNet, Squeezenet, and Mobilenet for transfer learning. Since the classification accuracies of these pre-trained models are not up to the expectations, hence, a custom designed architecture is proposed, which outperforms the pre-trained models giving 99.81 % classification accuracy. Hyper-parameters are varied during the experimentation and the results are compared with the state-of-the-art methods. The work is helpful for the practicing psychiatrists, in knowing what types of sounds manage, which kinds of stresses.
机译：压力管理是这个现代化世界的挑战，正在努力做几种方法，例如饮食，运动，睡眠，冥想和放松。其中一个放松方法是音乐疗法，其中一个听声乐音乐，乐器音乐，Hindustani古典音乐和西方音乐等。在仪器音乐中，使用各种类型的仪器，这些仪器因国家而异，这些仪器因国家而异不同的声音。基于声音，人类识别这些乐器，具有一定的功效，这些仪器与专业知识不同。在本文中，我们提出了14个类别的乐器，即钟声，大提琴，单簧管，克拉塔尔斯，双低音，笛子，钢琴，萨克斯管，长号，小号，Vibraphone，Viola，小提琴和木琴等的分类基于声音信号。使用总共8,365个谱图，横跨14个级别。我们已经部署了五种预先培训的CNN模型，如有效网络，Googlenet，Reset，Screezenet和MobileNet进行转移学习。由于这些预先训练的模型的分类准确性不符合预期，因此提出了一种定制设计的架构，这优于预先训练的模型，提供了99.81％的分类准确性。超级参数在实验期间变化，结果与最先进的方法进行了比较。这项工作有助于练习精神科医生，了解了什么类型的声音管理，这种压力。

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