Advancing Computational Musicology: Semi-supervised and unsupervised segmentation and annotation of musical collections (ACMus)

在过去几十年中，信息和通信技术的迅速发展转化为创造和管理数字文化遗产资源的大量研究和开发工作。虽然数字文化遗产时代带来了更广泛的文化内容可访问性，但它也带来了自己的挑战，包括长期获取信息，可持续性和数据量的快速增长。特别是对于音乐遗产，主要的挑战在于需要语义检索技术，能够将音乐元素，如我们的节奏，和声，旋律，音乐纹理和音色。 这种自动检索技术将为音乐学研究开辟新的范式，使音乐结构和相似性的大规模分析，节奏，旋律和和声模式识别，并分析音乐传统的时间演变。 有了这些技术，将提供新的机会，以加强现有的音乐学研究，从而提高效率，并为大规模的数据处理和可视化的新的可能性不可能的日期。本计画的目标是发展半监督与非监督的音乐语意检索方法，应用于音乐作品集的自动标注，重点在四个方面：音乐、语音与歌唱声的辨识、乐器合奏的辨识、音乐节拍的辨识与音阶的侦测。该工作计划结合并适应半监督和无监督技术的音乐结构注释，声学场景分割，环境音频标记，以前的工作调音和语调分析，乐器分类和参数化，音乐/语音歧视，和歌声检测的目的。本项目预计将取得以下研究成果：· 高效可靠的机器学习方法，用于音乐数据的自动分割和分类（关于音乐合奏、节拍、音阶和音乐/语音识别），允许以最少的人为干预处理大量集合。· 基于本项目开发的方法，并经专家音乐学家验证的新颖而强大的音乐学分析工作流程。由于本研究的多学科性质，项目团队将由信号处理，机器学习，音乐信息检索MIR，音乐学和音乐领域的研究人员组成。 该项目将作为德国弗劳恩霍夫数字媒体技术研究所（IDMT）和伊尔梅瑙技术大学（Technische Universität Ilmenau）这两个机构与哥伦比亚安蒂奥基亚大学和玻利瓦尔教皇大学这两所大学之间的双边项目进行。

The rapid advancement of ICT technologies in the last decades has translated into numerous research and development efforts for creation and management of digital cultural heritage resources. While the era of digital cultural heritage has resulted in wider accessibility of cultural content, it has also come with its own challenges, which include long-term access to information, sustainability, and rapid growth of data volume. Particularly for musical heritage, the main challenge lays in the need for semantic retrieval techniques capable of incorporating musical elements such us rhythm, harmony, melody, musical texture, and timbre. Such automatic retrieval techniques will open new paradigms for musicological research, enabling large-scale analysis of musical structure and similarity, rhythmic, melodic and harmonic pattern recognition, and analysis of time evolution of music traditions. With these techniques, new opportunities for enhancing existing musicological research will be provided, leading to increased efficiency, and to new possibilities for large-scale data manipulation and visualization not possible to date. The goal of this project is the development of semi-supervised and unsupervised semantic music retrieval methods for the automatic annotation of musical collections, focusing on four aspects: music, speech and singing voice discrimination, musical instrument ensemble recognition, musical meter recognition, and musical scale detection. The work program combines and adapts semi-supervised and unsupervised techniques for purposes of music structure annotation, acoustic scene segmentation, and environmental audio tagging, previous work on tuning and intonation analysis, musical instrument classification and parametrization, music/speech discrimination, and singing voice detection. The following research outcomes are expected from this project:• Efficient and reliable machine learning methods for automatic segmentation and classification of musical data – with respect to musical ensemble, meter, scale, and music/speech discrimination - that allow processing of large collections with minimal human intervention.• Novel and powerful workflows for musicological analysis based on the methodologies developed in this project and validated by expert musicologists.Due to the multidisciplinary nature of this research, the project team will be composed of researchers in the fields of signal processing, machine learning, music information retrieval MIR, musicology, and music. It will be conducted as a bilateral project between two German institutions, Fraunhofer Institute for Digital Media Technology IDMT and Technische Universität Ilmenau, and two Colombian universities, Universidad de Antioquia and Universidad Pontificia Bolivariana.