【24h】

Learning optimal features for music transcription

机译:学习音乐转录的最佳功能

获取原文

摘要

This paper aims to design time-frequency representation (TFR) functions for automatic music transcription. It is desirable that the decomposition of those TFR functions are suitable for notes having variation of both pitch and spectral envelop over time. The Harmonic Adaptive Latent Component Analysis (HALCA) model adopted in this paper allows considering those two kinds of variations simultaneously. We evaluate the influence of three TFR functions including IIR, FIR filter bank semigram (FBSG) and constant-Q transform semigram in automatic music transcription task, on a database of popular and polyphonic classic music. The experiment results show that the filter bank based representations are suitable for multiple-instrument recordings and a CQT-based representation turns out to provide very accurate transcription for solo-instrument recordings.
机译:本文旨在设计用于自动音乐转录的时频表示(TFR)功能。期望这些TFR功能的分解适用于具有间距和光谱包围的变化的音符随时间。本文采用的谐波自适应潜在分析(HALCA)模型允许同时考虑这两种变化。我们在自动音乐转录任务中评估三个TFR功能的影响,包括IIR,FIR滤波器银行半资金(FBSG)和常数-Q变换半重组,在流行和Polyphonic经典音乐数据库中。实验结果表明,基于滤波器组的表示适用于多仪器记录,基于CQT的代表结果表明,为独奏仪器记录提供了非常准确的转录。

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