Proceedings of the 5th International Workshop on Digital Libraries for Musicology (DLfM), Paris, France, September 2018. Published by ACM's International Conference Proceedings Series (ICPS)
在产生音符时的表达变化传达了对配器和风格建模至关重要的信息。因此，它在计算机辅助浏览大量数字音乐语料库中起着至关重要的作用。然而，尽管从单个“普通”音符的记录中自动识别乐器被认为是一个解决的问题，但是乐器演奏技术（IPT）的自动识别在很大程度上仍然不发达。我们基准机器听系统查询的例子浏览143个扩展的IPT为16种仪器，共计469三重奏的仪器，静音，和技术。我们确定并讨论了显着优于传统的梅尔频率倒谱系数（MFCC）基线的三个必要条件：增加二阶散射系数来解释幅度调制，纳入长期的时间依赖性，和度量学习使用大利润最近邻（LMNN），以减少类内的变化。在Studio On Line（SOL）数据集上进行评估，我们获得了仪器识别（基线为89.0%）和IPT识别（基线为44.5%）的99.7%和61.0%的精度。我们解释这一收益通过使用非线性降维的实际可用性和可视化的定性评估。
英文摘要：
The expressive variability in producing a musical note conveys information essential to the modeling of orchestration and style. As such, it plays a crucial role in computer-assisted browsing of massive digital music corpora. Yet, although the automatic recognition of a musical instrument from the recording of a single "ordinary" note is considered a solved problem, automatic identification of instrumental playing technique (IPT) remains largely underdeveloped. We benchmark machine listening systems for query-by-example browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets of instrument, mute, and technique. We identify and discuss three necessary conditions for significantly outperforming the traditional mel-frequency cepstral coefficient (MFCC) baseline: the addition of second-order scattering coefficients to account for amplitude modulation, the incorporation of long-range temporal dependencies, and metric learning using large-margin nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition (baseline at 44.5%). We interpret this gain through a qualitative assessment of practical usability and visualization using nonlinear dimensionality reduction.
Time-Frequency Scattering Accurately Models Auditory Similarities Between Instrumental Playing Techniques
LS2N（南特数字科学实验室）；CNRS（法国国家科学研究中心）；Centrale Nantes（南特中央理工学院）；Nantes University（南特大学）；Lonofi；KTH Royal Institute of Technology（瑞典皇家理工学院）；Flatiron Institute（弗莱提荣研究所）
Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example
Peking University（北京大学）；Wangxuan Institute of Computer Technology, PKU（北京大学王选计算机研究所）；China Conservatory of Music（中国音乐学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Time delay embeddings to characterize the timbre of musical instruments using Topological Data Analysis: a study on synthetic and real data
Department of Systems and Control Engineering, Institute of Science Tokyo (former Tokyo Tech), Tokyo, Japan（系统与控制工程系，东京科学研究所（原东京技术大学），东京）；International Research Frontiers Initiative, Institute of Science Tokyo (former Tokyo Tech), Kanagawa, Japan（国际前沿研究计划，东京科学研究所（原东京技术大学），神奈川）
