This is the accepted version of the paper.This version of the publication may differ from the final published version. In musicology and music research generally, the increasing availability of digital music, storage capacities and computing power both enable and require new and intelligent systems. In the transition from traditional to digital musicology, many techniques and tools have been developed for the analysis of individual pieces of music, but large scale music data that are increasingly becoming available require research methods and systems that work on the collection-level and at scale. Although many relevant algorithms have been developed during the last 15 years of research in Music Information Retrieval, an integrated system that supports large-scale digital musicology research has so far been lacking. In the Digital Music Lab (DML) project, a collaboration between music librarians, musicologists, computer scientists, and human-computer interface specialists, the DML software system has been developed that fills this gap by providing intelligent large-scale music analysis with a user-friendly interactive interface supporting musicologists in their exploration and enquiry. The DML system empowers musicologists by addressing several challenges: distributed processing of audio and other music data, management of the data analysis process and results, remote analysis of data under copyright, logical inference on the extracted information and metadata, and visual web-based interfaces for exploring and querying the music collections. The DML system is scalable and based on Semantic Web technology and integrates into Linked Data with the vision of a distributed system that enabling music research across archives, libraries and other providers of music data. A first DML system prototype has been set up in collaboration with the British Library and I Like Music Ltd. This system has been used to analyse a diverse corpus of currently 250,000 music tracks. In this article we describe the DML system requirements, design, architecture, components, available data sources, explaining their interaction. We report use cases and applications with initial evaluations of the proposed system. Permanent repository link
The identification of structural differences between a music performance and the score is a challenging yet integral step of audioto-score alignment, an important subtask of music information retrieval. We present a novel method to detect such differences between the score and performance for a given piece of music using progressively dilated convolutional neural networks. Our method incorporates varying dilation rates at different layers to capture both short-term and long-term context, and can be employed successfully in the presence of limited annotated data. We conduct experiments on audio recordings of real performances that differ structurally from the score, and our results demonstrate that our models outperform standard methods for structure-aware audio-to-score alignment.
This is the accepted version of the paper.This version of the publication may differ from the final published version. Online is checked for eligibility for copyright before being made available in the live archive. URLs from City Research Online may be freely distributed and linked to from other web pages. Permanent repository link: Versions of researchThe version in City Research Online may differ from the final published version. Users are advised to check the Permanent City Research Online URL above for the status of the paper. EnquiriesIf you have any enquiries about any aspect of City Research Online, or if you wish to make contact with the author(s) of this paper, please email the team at publications@city.ac.uk. Combining Sources of Description for Approximating Music Similarity Ratings Daniel Wolff and Tillman WeydeCity University London, Department of Computing Northampton Square, London EC1V 0HB, UK daniel.wolff.1@soi.city.ac.ukAbstract. In this paper, we compare the effectiveness of basic acoustic features and genre annotations when adapting a music similarity model to user ratings. We use the Metric Learning to Rank algorithm to learn a Mahalanobis metric from comparative similarity ratings in in the MagnaTagATune database. Using common formats for feature data, our approach can easily be transferred to other existing databases. Our results show a notable correlation between songs' genres and associated similarity ratings, but learning on a combined feature set clearly outperforms either individual approach.
This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract. In this paper, we compare the effectiveness of basic acoustic features and genre annotations when adapting a music similarity model to user ratings. We use the Metric Learning to Rank algorithm to learn a Mahalanobis metric from comparative similarity ratings in in the MagnaTagATune database. Using common formats for feature data, our approach can easily be transferred to other existing databases. Our results show a notable correlation between songs' genres and associated similarity ratings, but learning on a combined feature set clearly outperforms either individual approach. Permanent
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