Musical onset detection on carnatic percussion instruments

M. Kumar, J. Sebastian, H. Murthy
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引用次数: 18

Abstract

In this work, we explore the task of musical onset detection in Carnatic music by choosing five major percussion instruments: the mridangam, ghatam, kanjira, morsing and thavil. We explore the musical characteristics of the strokes for each of the above instruments, motivating the challenge in designing an onset detection algorithm. We propose a non-model based algorithm using the minimum-phase group delay for this task. The music signal is treated as an Amplitude-Frequency modulated (AM-FM) waveform, and its envelope is extracted using the Hilbert transform. Minimum phase group delay processing is then applied to accurately determine the onset locations. The algorithm is tested on a large dataset with both controlled and concert recordings (tani avarthanams). The performance is observed to be the comparable with that of the state-of-the-art technique employing machine learning algorithms.
卡纳蒂克打击乐器的音乐开始检测
在这项工作中,我们通过选择五种主要的打击乐器:mridangam, ghatam, kanjira, morsing和thavil来探索卡纳蒂克音乐中音乐开始检测的任务。我们探讨了上述每种乐器笔画的音乐特征,激发了设计一种起音检测算法的挑战。我们提出了一种使用最小相位群延迟的非基于模型的算法。将音乐信号作为幅频调制(AM-FM)波形处理,利用希尔伯特变换提取其包络。然后应用最小相位群延迟处理来精确确定起始位置。该算法在大型数据集上进行了测试,其中包括受控和音乐会录音(tani avarthanams)。其性能可与采用机器学习算法的最先进技术相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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