• Corpus ID: 246411686

Exploring Graph Representation of Chorales

  title={Exploring Graph Representation of Chorales},
  author={Somnuk Phon-Amnuaisuk},
This work explores areas overlapping music, graph theory, and machine learning. An embedding representation of a node, in a weighted undirected graph G, is a representation that captures the meaning of nodes in an embedding space. In this work, 383 Bach chorales were compiled and represented as a graph. Two application cases were investigated in this paper (i) learning node embedding representation using Continuous Bag of Words (CBOW), skip-gram, and node2vec algorithms, and (ii) learning node… 

Figures and Tables from this paper



A Measure of Melodic Similarity based on a Graph Representation of the Music Structure

A novel similarity measure between melodic content, as represented in symbolic notation, that takes into account musicological aspects on the structural function of the melodic elements is proposed.

node2vec: Scalable Feature Learning for Networks

In node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks, a flexible notion of a node's network neighborhood is defined and a biased random walk procedure is designed, which efficiently explores diverse neighborhoods.

Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance

This paper designs the model using note-level gated graph neural network and measure-level hierarchical attention network with bidirectional long shortterm memory with an iterative feedback method and applies it for rendering expressive piano performance from the music score.

word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

This note is an attempt to explain equation (4) (negative sampling) in "Distributed Representations of Words and Phrases and their Compositionality" by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado and Jeffrey Dean.

Software Framework for Topic Modelling with Large Corpora

This work describes a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion, and implements several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation in a way that makes them completely independent of the training corpus size.

Music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data

This paper introduces the music21 system, demonstrating how to use it and the types of problems it is wellsuited toward advancing, and includes numerous examples of its power and flexibility.

Hierarchical music representation for composition and analysis

It is concluded that it is possible and useful to represent music in a way independent of the particular style, tonal system, etc., of the music itself.

Efficient Estimation of Word Representations in Vector Space

Two novel model architectures for computing continuous vector representations of words from very large data sets are proposed and it is shown that these vectors provide state-of-the-art performance on the authors' test set for measuring syntactic and semantic word similarities.

Chorale harmonization: A view from a search control perspective

To overcome the intractability problem, this report proposes a careful knowledge engineering approach that offers a useful language specialized for the chorale harmonization task through its three primitives, namely: rules, tests and measures.

Virtual Music: Computer Synthesis of Musical Style

Virtual Music is about artificial creativity. Focusing on the author's Experiments in Musical Intelligence computer music composing program, the author and a distinguished group of experts discuss