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 musical pattern


Combinatorial music generation model with song structure graph analysis

Go, Seonghyeon, Lee, Kyogu

arXiv.org Artificial Intelligence

In this work, we propose a symbolic music generation model with the song structure graph analysis network. We construct a graph that uses information such as note sequence and instrument as node features, while the correlation between note sequences acts as the edge feature. We trained a Graph Neural Network to obtain node representation in the graph, then we use node representation as input of Unet to generate CONLON pianoroll image latent. The outcomes of our experimental results show that the proposed model can generate a comprehensive form of music. Our approach represents a promising and innovative method for symbolic music generation and holds potential applications in various fields in Music Information Retreival, including music composition, music classification, and music inpainting systems.


jazznet: A Dataset of Fundamental Piano Patterns for Music Audio Machine Learning Research

Adegbija, Tosiron

arXiv.org Artificial Intelligence

This paper introduces the jazznet Dataset, a dataset of fundamental jazz piano music patterns for developing machine learning (ML) algorithms in music information retrieval (MIR). The dataset contains 162520 labeled piano patterns, including chords, arpeggios, scales, and chord progressions with their inversions, resulting in more than 26k hours of audio and a total size of 95GB. The paper explains the dataset's composition, creation, and generation, and presents an open-source Pattern Generator using a method called Distance-Based Pattern Structures (DBPS), which allows researchers to easily generate new piano patterns simply by defining the distances between pitches within the musical patterns. We demonstrate that the dataset can help researchers benchmark new models for challenging MIR tasks, using a convolutional recurrent neural network (CRNN) and a deep convolutional neural network. The dataset and code are available via: https://github.com/tosiron/jazznet.


Why A.I. Will Not Take Over Music

#artificialintelligence

It was early morning and Walks With Moon hard the faint rhythm of the drums far off in the distance. He stood still and cocked an ear, listening intently. When he understood the meaning, he ran to the area his tribe was making home, looking for the elders. He told them that he'd heard the drums, that the first message for a PowWow had started. They gathered their drums, headed out of the camp and moved to a small clearing closer in distance to where Walks With Moon had heard the message and they began to reply with their own message. So what does this have to do with Artificial Intelligence?