Multi-Pitch Estimation using NHF with Multi-Dictionary Distinguishing Attack and Reverberation of Sounds
Multi-Pitch Estimation using NHF with Multi-Dictionary Distinguishing Attack and Reverberation of Sounds
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DOI：
10.1109/ieeeconf44664.2019.9048668
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Takanori Fujisawa;Sora Harada;M. Ikehara
中科院分区：
文献类型：
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作者：
Takanori Fujisawa;Sora Harada;M. Ikehara
本文提出了一种钢琴音乐的多音高估计算法，提高了精度和处理时间。传统的方法采用非负矩阵分解（NMF）和奇异值分解来保证音乐的声音和模式的连续性。然而，音频信号谱图具有较大的元素，并且处理奇异值分解在计算时间上是低效的。我们的方法将输入的音频频谱图分离成声音块。然后，我们应用一个NMF与组稀疏约束，以加强声音的连续性。此外，我们使用不同的字典的攻击部分和混响部分的声音。提高了各部分估计的精度。
This paper proposes a multiple pitch estimation algorithm for the piano music which improves both precision and processing time. The conventional method applies non-negative matrix factorization (NMF) and singular value decomposition to ensure the continuity of sound and pattern of musics. However, audio signal spectrogram has large elements and processing singular value decomposition is inefficient in the computational time. Our method separates the input audio spectrogram into the block of sound. Then we apply a NMF with group sparsity constraint to enforce the continuity of sound. In addition, we use the different dictionaries for the attack part and the reverberation part of the sound. It improves the precision of the estimation for each part.