Linked Data and You: Bringing Music Research Software into the Semantic Web

Cannam, Chris; Sandler, Mark; Jewell, Michael O.; Rhodes, Christophe; d’Inverno, Mark

doi:10.1080/09298215.2010.522715

Cited by 32 publications

(25 citation statements)

References 6 publications

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“…Repeated sets are selected based on a threshold for the amount of repetition (as repeated segments will not be 100% identical for certain music styles). Both the bar tracking and segmentation algorithms are implemented as Vamp plugins for the Sonic Annotator host [18].…”

Section: Editing Methodsmentioning

confidence: 99%

Music Thumbnailing for Radio Podcasts: A Listener Evaluation

Mehrabi¹,

Harte²,

Baume³

et al. 2017

J. Audio Eng. Soc.

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When radio podcasts are produced from previously broadcast material, 30-second "thumbnails" of songs that featured in the original program are often included. Such thumbnails are made up of continuous or concatenated sections from a song and provide the audience with a summary of the music content. However, editing full-length songs down to representative thumbnails is a labor intensive process, particularly when concatenating multiple song sections. This presents an ideal application for automatic music editing tools and raises the question of how a piece of music is best summarized for this task. To gain insight into this problem we asked 120 listeners to rate the quality of thumbnails generated by eight methods (five automatic and three manual). When asked to judge overall editing quality (on a five point Likert scale) listeners gave higher ratings to methods where the edit points were quantized to bar positions, although we found no preference for structural content such as the chorus. Ratings for two automatic editing methods (one containing the chorus, one containing only the intro and outro) were not significantly different to their manual counterparts. This result suggests that the automatic editing methods applied here can be used to create production quality thumbnails.

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Section: Editing Methodsmentioning

confidence: 99%

Music Thumbnailing for Radio Podcasts: A Listener Evaluation

Mehrabi¹,

Harte²,

Baume³

et al. 2017

J. Audio Eng. Soc.

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“…e layer takes the form of an extended work ow of Python scripts 10 built around Sonic Annotator [9], a tool for batch analysis of audio which performs feature estimation and extraction from audio les by the application of analysis modules known as Vamp plugins.…”

Section: Computational Analysis: Feature Extractionmentioning

confidence: 99%

Realising a Layered Digital Library: Exploration and Analysis of the Live Music Archive through Linked Data

Page

Bechhofer

Fazekas

et al. 2017

2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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Building upon a collection with functionality for discovery and analysis has been described by Lynch as a 'layered' approach to digital libraries. Meanwhile, as digital corpora have grown in size, their analysis is necessarily supplemented by automated application of computational methods, which can create layers of information as intricate and complex as those within the content itself. is combination of layers -aggregating homogeneous collections, specialised analyses, and new observations -requires a exible approach to systems implementation which enables pathways through the layers via common points of understanding, while simultaneously accommodating the emergence of previously unforeseen layers.In this paper we follow a Linked Data approach to build a layered digital library based on content from the Internet Archive Live Music Archive. Starting from the recorded audio and basic information in the Archive, we rst deploy a layer of catalogue metadata which allows an initial -if imperfect -consolidation of performer, song, and venue information. A processing layer extracts audio features from the original recordings, work ow provenance, and summary feature metadata. A further analysis layer provides tools for the user to combine audio and feature data, discovered and reconciled using interlinked catalogue and feature metadata from layers below.Finally, we demonstrate the feasibility of the system through an investigation of 'key typicality' across performances.is highlights the need to incorporate robustness to inevitable 'imperfections' when undertaking scholarship within the digital library, be that from mislabelling, poor quality audio, or intrinsic limitations of computational methods. We do so not with the assumption that a 'perfect' version can be reached; but that a key bene t of a layered approach is to allow accurate representations of information to be discovered, combined, and investigated for informed interpretation.

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“…For example, DBTune.org links several music-related data sources on the Semantic Web [7], among them MusicBrainz [8], MySpace, and BBC Music. Other works have looked into publishing music recording metadata, and results from audio analysis algorithms [2]. Although the publication of music metadata about artists, songs, albums, musical events, and experimental results is an obvious contribution to the variety of Linked Data, music itself is currently only composed, published, and exchanged offline or using monolithic systems.…”

Section: Introductionmentioning

confidence: 99%

The Song Remains the Same: Lossless Conversion and Streaming of MIDI to RDF and Back

Meroño‐Peñuela

Hoekstra

2016

The Semantic Web

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Abstract. In this demo, we explore the potential of RDF as a representation format for digital music. Digital music is broadly used today in many professional music production environments. For decades, MIDI (Musical Instrument Digital Interface) has been the standard for digital music exchange between musicians and devices, albeit not in a Web friendly way. We show the potential of expressing digital music as Linked Data, using our midi2rdf suite of tools to convert and stream digital music in MIDI format to RDF. The conversion allows for lossless round tripping: we can reconstruct a MIDI file identical to the original using its RDF representation. The streaming uses an existing, novel generative audio matching algorithm that we use to broadcast, with very low latency, RDF triples of MIDI events coming from arbitrary analog instruments.

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Linked Data and You: Bringing Music Research Software into the Semantic Web

Cited by 32 publications

References 6 publications

Music Thumbnailing for Radio Podcasts: A Listener Evaluation

Music Thumbnailing for Radio Podcasts: A Listener Evaluation

Realising a Layered Digital Library: Exploration and Analysis of the Live Music Archive through Linked Data

The Song Remains the Same: Lossless Conversion and Streaming of MIDI to RDF and Back

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