Digital Music Lab - Analysing Big Music Data

音乐研究，特别是在系统音乐学、民族音乐学或音乐心理学等领域，已经发展成为“以数据为导向的实证研究”，这得益于计算方法。特别是在民族音乐学中，最近人们对计算音乐学及其在音频数据收集中的应用越来越感兴趣。同样，对西方音乐表现的实证研究，如时间、动态、音色及其与音乐结构的关系，也有着悠久的传统。然而，由于技术和法律的限制，到目前为止，这种音乐研究仅限于相对较小的数据集。另一方面，音乐信息检索（MIR）的研究人员已经开始探索大型数据集，特别是在商业推荐和播放列表系统（例如Echo Nest， Spotify）中，但是MIR和音乐学之间在术语，方法和目标以及技术和法律障碍方面存在差异。拟议中的数字音乐实验室将通过弥合与MIR之间的差距，并使访问大型音乐收藏和强大的分析和可视化工具成为可能，从而支持音乐研究。数字音乐实验室项目将开发研究方法和软件基础设施，用于探索和分析大规模的音乐收藏。该项目的一个主要产出将是一个服务基础设施，其中有两个原型装置。一个装置将使研究人员、音乐家和一般用户能够探索、分析和提取存储在大英图书馆（BL）的音乐录音中的信息。另一个装置将由伦敦玛丽女王大学的数字音乐中心主办，并提供设备来分析音频集合，如“我喜欢音乐”、“魅力”和“等音波”数据集，创建一个规模可观的数据集合（超过100万件）。我们将为研究人员提供分析音乐音频、乐谱和元数据的工具。将音频和符号层面的最先进音乐分析与智能收藏层面的分析方法相结合，将允许对音乐进行探索和定量研究，这在目前的规模上是不可能的。这些分析的结果将以高度互动的视觉界面的形式提供。我们将探讨的音乐问题包括：在古典音乐、世界音乐、爵士音乐或流行音乐中，表演风格如何随着时间的推移而变化；一种特定类型的表演如何因地理位置而异？表演者的个人表演审美在其一生中是如何发展的；我们如何确定一个表演者对另一个表演者的影响。分析的出发点将是钢琴音乐和民歌中的音乐节奏和结构问题。我们还将探索更多的通用音乐学问题，例如在收集级别上使用数据挖掘特定乐器在不同文化中的作用，例如在信号和元数据级别上关联相似性。使用所有开放标准（如Music Ontology），可以聚合用于研究用途的结果派生数据，以及带有元数据的音频文件注释。拟议框架的使用将在古典音乐的音乐学研究（建立在ahrc资助的CHARM和CMPCP研究中心的基础上）以及民间、世界和流行音乐中得到证明。所有结果将作为开放数据/开源软件提供。我们认为这个项目有潜力将音乐学和MIR社区聚集在一起，实现互利共赢。

Music research, particularly in fields like systematic musicology, ethnomusicology, or music psychology, has developed as "data oriented empirical research", which benefits from computing methods. In ethnomusicology particularly, there has been a recent growing interest in computational musicology and its application to audio data collections. Similarly, the empirical study of performance of Western music, such as timing, dynamics and timbre and their relation to musical structure has a long tradition. However, this music research has so far been limited to relatively small datasets, because of technological and legal limitations. On the other hand, researchers in Music Information Retrieval (MIR) have started to explore large datasets, particularly in commercial recommendation and playlisting systems (e.g. The Echo Nest, Spotify), but there are differences in the terminologies, methods, and goals between MIR and musicology as well as technological and legal barriers. The proposed Digital Music Lab will support music research by bridging the gap to MIR and enabling access to large music collections and powerful analysis and visualization tools. The Digital Music Lab project will develop research methods and software infrastructure for exploring and analysing large-scale music collections. A major output of the project will be a service infrastructure with two prototype installations. One installation will enable researchers, musicians and general users to explore, analyse and extract information from music recordings stored in the British Library (BL). Another installation will be hosted by the Centre for Digital Music at Queen Mary University of London and provide facilities to analyse audio collections such as the I Like Music, CHARM and the Isopohnics datasets, creating a data collection of significant size (over 1m pieces). We will provide researchers with the tools to analyse music audio, scores and metadata. The combination of state-of-the-art music analysis on the audio and the symbolic level with intelligent collection-level analysis methods will allow for exploration and quantitative research on music that has not been not possible at this scale so far. The results of these analyses will be made available in the form of highly interactive visual interfaces. Musical questions we will explore include: how does performance style change change over time in relation to a particular genre or style, in classical, world, jazz, or popular music; how might performances of a given genre vary by geographical location; how does a performer's individual performance aesthetic develop over their lifetime; how might we identify the influence of one performer on another. Starting points for the analysis will be questions of musical timing and structure in piano music as well as in folk songs. We will also explore more generic musicological questions, such as the role of specific instruments in different cultures using data mining on the collection level, e.g. for relating similarities on the signal and metadata level. The resulting derived data that can be aggregated for research use, and the annotation of audio files with metadata, using all open standards such as the Music Ontology. The use of the proposed framework will be demonstrated in musicological research on classical music (building on the AHRC-funded CHARM and CMPCP research centres), as well as in folk, world and popular music. All results will be made available as open data/open source software. We feel that this project has the potential to bring together communities from musicology and MIR to mutual benefit.