分割,转录,分析和可视化的挪威民间音乐档案

O. Lartillot, Anders Elowsson, Mats Johansson, H. Thedens, Lars Monstad
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引用次数: 1

摘要

我们提出了一个正在进行的项目,致力于将20世纪60年代建立的挪威民间音乐的现场录音集合转化为一个易于访问的在线目录,增强了先进的音乐技术和计算机音乐学工具。我们特别关注这个集合的一个主要亮点:Hardanger小提琴音乐。所研究的语料库以一系列600个磁带录音的形式提供,每个磁带包含长达2小时的录音,并与指示音乐片段大致位置的元数据相关联。我们首先需要通过基于声音分类和音频分析的自动预分割,以及随后使用自制用户界面对时间位置进行手动验证和微调,来检索与每个曲调相关的单个录音。音符检测是通过深度学习方法进行的。为了使模型适应Hardanger的小提琴音乐,音乐家们被要求用一个专用的界面记录自己并注释所有演奏的音符。数据增强技术已经被设计用来加速这一过程,特别是使用同一曲调的不同表演的对齐。抄写也需要重建格律结构,这在这种风格的音乐中尤其具有挑战性。我们还收集了真实的数据,并正在构思一个计算模型。下一步是对乐谱进行详细的音乐分析,以揭示语料库中的互文性。研究的最后一个方向是为音乐学家和普通大众设计可视化每首曲子和整个目录的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation, Transcription, Analysis and Visualisation of the Norwegian Folk Music Archive
We present an ongoing project dedicated to the transmutation of a collection of field recordings of Norwegian folk music established in the 1960s into an easily accessible online catalogue augmented with advanced music technology and computer musicology tools. We focus in particular on a major highlight of this collection: Hardanger fiddle music. The studied corpus was available as a series of 600 tape recordings, each tape containing up to 2 hours of recordings, associated with metadata indicating approximate positions of pieces of music. We first need to retrieve the individual recording associated with each tune, through the combination of an automated pre-segmentation based on sound classification and audio analysis, and a subsequent manual verification and fine-tuning of the temporal positions, using a home-made user interface. Note detection is carried out by a deep learning method. To adapt the model to Hardanger fiddle music, musicians were asked to record themselves and annotate all played note, using a dedicated interface. Data augmentation techniques have been designed to accelerate the process, in particular using alignment of varied performances of same tunes. The transcription also requires the reconstruction of the metrical structure, which is particularly challenging in this style of music. We have also collected ground-truth data, and are conceiving a computational model. The next step consists in carrying out detailed music analysis of the transcriptions, in order to reveal in particular intertextuality within the corpus. A last direction of research is aimed at designing tools to visualise each tune and the whole catalogue, both for musicologists and general public.
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