Scales, Chords, and Cadences: Practical Music Theory for MIR Researchers

Overview

ISMIR-musicTheoryTutorial

This repository has slides and Jupyter notebooks for the ISMIR 2021 tutorial Scales, Chords, and Cadences: Practical Music Theory for MIR Researchers (https://ismir2021.ismir.net/tutorials/#3-scales-chords-and-cadences-practical-music-theory-for-mir-researchers)

Tutorial Bibliography

https://www.zotero.org/groups/4502273/ismir-musictheorytutorial

Tutorial Description

Much pitch-related MIR research builds either implicitly or explicitly on music-theoretic domain knowledge. Unfortunately, music theory is an esoteric discipline, with many of its canonical organizational principles presented in textbooks with dozens of classical musical examples and little indication of how these principles can be applied to other musical traditions. This tutorial will introduce fundamental pitch-related concepts in music theory for the ISMIR community and relate them to tasks associated with melodic, chord, and structural audio analysis for a range of musical styles. It will include sections on the scales, chords, and cadences routinely associated with Western art music of the common-practice tradition (~1650-1900), as well as non-Western folk musics and the popular music traditions of the twentieth and twenty-first centuries. The three sections will be broken down as follows, with both lecture and hands-on coding demonstration components:

Scales

-Scale formation (octave equivalence, mathematical properties)

-Scale and mode types (western and non-Western)

-Implications for scale and key identification, automatic melody extraction

Chords

-Types (triads, seventh chords, extensions)

-Representation schemes (e.g., chord labeling)

-Syntactic principles (e.g., functional harmony, grammars)

-Implications for automatic chord recognition, pattern discovery

Cadences

-Types

-Linear/voice-leading patterns

-Relationship to large-scale formal types (phrases, themes, sonata, etc.)

-Implications for cadence discovery/classification, automatic segmentation

This tutorial will be of interest to a broad range of the ISMIR community, but will be of specific interest to MIR researchers with limited formal training in music theory. This workshop assumes a basic understanding of musical notation, but does not assume prior knowledge of Western music theory. It will be accessible to researchers new to the field, but will also be of interest to experienced researchers hoping to incorporate more music-theoretically based models into their research.

Owner
Johanna Devaney
Johanna Devaney
Deep generative models of 3D grids for structure-based drug discovery

What is liGAN? liGAN is a research codebase for training and evaluating deep generative models for de novo drug design based on 3D atomic density grid

Matt Ragoza 152 Jan 03, 2023
A repository for benchmarking neural vocoders by their quality and speed.

License The majority of VocBench is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Wavenet, Para

Meta Research 177 Dec 12, 2022
Code for BMVC2021 paper "Boundary Guided Context Aggregation for Semantic Segmentation"

Boundary-Guided-Context-Aggregation Boundary Guided Context Aggregation for Semantic Segmentation Haoxiang Ma, Hongyu Yang, Di Huang In BMVC'2021 Pape

Haoxiang Ma 31 Jan 08, 2023
A coin flip game in which you can put the amount of money below or equal to 1000 and then choose heads or tail

COIN_FLIPPY ##This is a simple example package. You can use Github-flavored Markdown to write your content. Coinflippy A coin flip game in which you c

2 Dec 26, 2021
Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

Accompanying code for the paper Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

Kevin Wilkinghoff 6 Dec 01, 2022
Food recognition model using convolutional neural network & computer vision

Food recognition model using convolutional neural network & computer vision. The goal is to match or beat the DeepFood Research Paper

Hemanth Chandran 1 Jan 13, 2022
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".

Deep Exemplar-based Video Colorization (Pytorch Implementation) Paper | Pretrained Model | Youtube video 🔥 | Colab demo Deep Exemplar-based Video Col

Bo Zhang 253 Dec 27, 2022
Understanding Convolution for Semantic Segmentation

TuSimple-DUC by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. Introduction This repository is for Under

TuSimple 585 Dec 31, 2022
Pytorch implementation of NEGEV method. Paper: "Negative Evidence Matters in Interpretable Histology Image Classification".

Pytorch 1.10.0 code for: Negative Evidence Matters in Interpretable Histology Image Classification (https://arxiv. org/abs/xxxx.xxxxx) Citation: @arti

Soufiane Belharbi 4 Dec 01, 2022
Source code for our Paper "Learning in High-Dimensional Feature Spaces Using ANOVA-Based Matrix-Vector Multiplication"

NFFT4ANOVA Source code for our Paper "Learning in High-Dimensional Feature Spaces Using ANOVA-Based Matrix-Vector Multiplication" This package uses th

Theresa Wagner 1 Aug 10, 2022
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayes

Intel Labs 210 Jan 04, 2023
Demo code for paper "Learning optical flow from still images", CVPR 2021.

Depthstillation Demo code for "Learning optical flow from still images", CVPR 2021. [Project page] - [Paper] - [Supplementary] This code is provided t

130 Dec 25, 2022
💃 VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena

💃 VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena.

Heidelberg-NLP 17 Nov 07, 2022
Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

CoProtector Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

Zhensu Sun 1 Oct 26, 2021
The code for replicating the experiments from the LFI in SSMs with Unknown Dynamics paper.

Likelihood-Free Inference in State-Space Models with Unknown Dynamics This package contains the codes required to run the experiments in the paper. Th

Alex Aushev 0 Dec 27, 2021
Pytorch Implementation of PointNet and PointNet++++

Pytorch Implementation of PointNet and PointNet++ This repo is implementation for PointNet and PointNet++ in pytorch. Update 2021/03/27: (1) Release p

Luigi Ariano 1 Nov 11, 2021
Code repository for our paper regarding the L3D dataset.

The Large Labelled Logo Dataset (L3D): A Multipurpose and Hand-Labelled Continuously Growing Dataset Website: https://lhf-labs.github.io/tm-dataset Da

LHF Labs 9 Dec 14, 2022
The open-source and free to use Python package miseval was developed to establish a standardized medical image segmentation evaluation procedure

miseval: a metric library for Medical Image Segmentation EVALuation The open-source and free to use Python package miseval was developed to establish

59 Dec 10, 2022
Deep learning library featuring a higher-level API for TensorFlow.

TFLearn: Deep learning library featuring a higher-level API for TensorFlow. TFlearn is a modular and transparent deep learning library built on top of

TFLearn 9.6k Jan 02, 2023
The repository for our EMNLP 2021 paper "Finnish Dialect Identification: The Effect of Audio and Text"

Finnish Dialect Identification The repository for our EMNLP 2021 paper "Finnish Dialect Identification: The Effect of Audio and Text". We present a te

Rootroo Ltd 2 Dec 25, 2021