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
RTSeg: Real-time Semantic Segmentation Comparative Study

Real-time Semantic Segmentation Comparative Study The repository contains the official TensorFlow code used in our papers: RTSEG: REAL-TIME SEMANTIC S

Mennatullah Siam 592 Nov 18, 2022
Learned image compression

Overview Pytorch code of our recent work A Unified End-to-End Framework for Efficient Deep Image Compression. We first release the code for Variationa

Jiaheng Liu 163 Dec 04, 2022
This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Koltun"

Learning to propose objects This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Ko

Philipp Krähenbühl 90 Sep 10, 2021
Pytorch implement of 'Unmixing based PAN guided fusion network for hyperspectral imagery'

Pgnet There's a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block: https://arxiv.org/abs

5 Jul 01, 2022
EasyMocap is an open-source toolbox for markerless human motion capture from RGB videos.

EasyMocap is an open-source toolbox for markerless human motion capture from RGB videos. In this project, we provide the basic code for fitt

ZJU3DV 2.2k Jan 05, 2023
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings

When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings This is the repository for t

RegLab 39 Jan 07, 2023
Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks

OnsagerNet Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks This is the original pyTorch implemenati

Haijun.Yu 3 Aug 24, 2022
Emotion classification of online comments based on RNN

emotion_classification Emotion classification of online comments based on RNN, the accuracy of the model in the test set reaches 99% data: Large Movie

1 Nov 23, 2021
RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds This repository contains the code asscoiated

Felix Hensel 14 Dec 12, 2022
This is the official implementation for the paper "(Almost) Free Incentivized Exploration from Decentralized Learning Agents" in NeurIPS 2021.

Observe then Incentivize Experiments This is the code used for the paper "(Almost) Free Incentivized Exploration from Decentralized Learning Agents",

Cong Shen Research Group 0 Mar 08, 2022
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

Rishik Mourya 48 Dec 20, 2022
Tutorial to set up TensorFlow Object Detection API on the Raspberry Pi

A tutorial showing how to set up TensorFlow's Object Detection API on the Raspberry Pi

Evan 1.1k Dec 26, 2022
Interactive Visualization to empower domain experts to align ML model behaviors with their knowledge.

An interactive visualization system designed to helps domain experts responsibly edit Generalized Additive Models (GAMs). For more information, check

InterpretML 83 Jan 04, 2023
Minimalistic PyTorch training loop

Backbone for PyTorch training loop Will try to keep it minimalistic. pip install back from back import Bone Features Progress bar Checkpoints saving/l

Kashin 4 Jan 16, 2020
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

Xinyan Zhao 29 Dec 26, 2022
Repository for MDPGT

MD-PGT Repository for implementing and reproducing the results for the paper MDPGT: Momentum-based Decentralized Policy Gradient Tracking. Available E

Xian Yeow Lee 2 Dec 30, 2021
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective

Unofficial pytorch implementation of the paper "Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective"

16 Nov 21, 2022
Users can free try their models on SIDD dataset based on this code

SIDD benchmark 1 Train python train.py If you want to train your network, just modify the yaml in the options folder. 2 Validation python validation.p

Yuzhi ZHAO 2 May 20, 2022
This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation

This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation (Guillaume Couairon, Holger

Meta Research 31 Oct 17, 2022
DiAne is a smart fuzzer for IoT devices

Diane Diane is a fuzzer for IoT devices. Diane works by identifying fuzzing triggers in the IoT companion apps to produce valid yet under-constrained

seclab 28 Jan 04, 2023