Encoding medieval music notation for research

Stinson, John; Stoessel, Jason

doi:10.1093/em/cau093

Cited by 3 publications

(2 citation statements)

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“…The Music Extensible Markup Language (MusicXML) is an open file format based on XML for encoding Western sheet music. The format is open for recording and can be freely used in accordance with the W3C community's license agreement [26,27]. The most common file format for sheet music is MIDI, which can represent complex compositions and is relatively playable.…”

Section: Musicxml Datasetmentioning

confidence: 99%

CVAE-GAN Emotional AI Music System for Car Driving Safety

Huang¹,

Huang²

2022

Intelligent Automation &Amp; Soft Computing

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Musical emotion is important for the listener's cognition. A smooth emotional expression generated through listening to music makes driving a car safer. Music has become more diverse and prolific with rapid technological developments. However, the cost of music production remains very high. At present, because the cost of music creation and the playing copyright are still very expensive, the music that needs to be listened to while driving can be executed by the way of automated composition of AI to achieve the purpose of driving safety and convenience. To address this problem, automated AI music composition has gradually gained attention in recent years. This study aims to establish an automated composition system that integrates music, emotion, and machine learning. The proposed system takes a music database with emotional tags as input, and deep learning trains the conditional variational autoencode generative adversarial network model as a framework to produce musical segments corresponding to the specified emotions. The system takes the music database with emotional tags as input, and deep learning trains the CVAE-GAN model as the framework to produce the music segments corresponding to the specified emotions. Participants listen to the results of the system and judge whether the music corresponds to their original emotion.

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Section: Musicxml Datasetmentioning

confidence: 99%

CVAE-GAN Emotional AI Music System for Car Driving Safety

Huang¹,

Huang²

2022

Intelligent Automation &Amp; Soft Computing

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“…We selected the MEI format [36] for outputting Agostini's encoded scores due to strong community support for this open-source music encoding project and based upon perceived benefits for the future extension of IntelliOMR's capabilities to export to the Mensural and Neume modules of MEI [52,53]. The open-source Verovio MEI Viewer [45] has also proven to be a useful online tool for verifying encoded notation visually prior to output to file.…”

Section: Mei Exportmentioning

confidence: 99%

Using Optical Music Recognition to Encode 17th-Century Music Prints

Stoessel

Collins

Bolland

2020

7th International Conference on Digital Libraries for Musicology

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There have been several attempts to improve the retrieval of symbolic music information by Optical Music Recognition (OMR) to increase the "searchability" of digital music libraries of early music prints and to facilitate the collection of data for musicological research. Their success has varied. This report describes a new online OMR system based upon industry-standard platforms to automate the encoding of early 17th-century music prints. Due to our research on composers of canons in early 17th-century Rome, we have used as a test case the early music prints of Paolo Agostini. Agostini was maestro di cappella at St Peter's Basilica and the most active exponent of advanced contrapuntal techniques, especially canon, in Rome in the 1620s. We developed a digital tool to process images of Agostini's printed music and to classify 7,092 automatically selected objects according to 38 music symbols using supervised learning with convolutional neural networks (CNN). The resulting system, IntelliOMR, exhibits up to an average of 99% accuracy for classifying unseen items after 50 training epochs. It has proven effective for rapidly encoding all of Agostini's works in the Music Encoding Initiative's XML format for a critical edition and computer-assisted musical analysis. The approach and design of this digital tool offer significant opportunities for enhancing digital library systems and for future research projects investigating digital corpora of early printed music. CCS CONCEPTS • Applied computing → Sound and music computing; • Humancentered computing → Collaborative and social computing; • Information systems → Data encoding and canonicalization.

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Digital matters: early Iberian manuscripts from the Lisbon vantage point

Ferreira

2022

Journal of Medieval Iberian Studies

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Encoding medieval music notation for research

Cited by 3 publications

References 0 publications

CVAE-GAN Emotional AI Music System for Car Driving Safety

CVAE-GAN Emotional AI Music System for Car Driving Safety

Using Optical Music Recognition to Encode 17th-Century Music Prints

Digital matters: early Iberian manuscripts from the Lisbon vantage point

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