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
Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)

Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)

Jiaxi Jiang 282 Jan 02, 2023
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

DV Lab 137 Dec 14, 2022
Publication describing 3 ML examples at NSLS-II and interfacing into Bluesky

Machine learning enabling high-throughput and remote operations at large-scale user facilities. Overview This repository contains the source code and

BNL 4 Sep 24, 2022
Code for 1st place solution in Sleep AI Challenge SNU Hospital

Sleep AI Challenge SNU Hospital 2021 Code for 1st place solution for Sleep AI Challenge (Note that the code is not fully organized) Refer to the notio

Saewon Yang 13 Jan 03, 2022
Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

Graph Convolutional Networks for Hyperspectral Image Classification Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot T

Danfeng Hong 154 Dec 13, 2022
LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs.

LocUNet LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs. The method utilizes accura

4 Oct 05, 2022
Powerful and efficient Computer Vision Annotation Tool (CVAT)

Computer Vision Annotation Tool (CVAT) CVAT is free, online, interactive video and image annotation tool for computer vision. It is being used by our

OpenVINO Toolkit 8.6k Jan 01, 2023
Platform-agnostic AI Framework 🔥

🇬🇧 TensorLayerX is a multi-backend AI framework, which can run on almost all operation systems and AI hardwares, and support hybrid-framework progra

TensorLayer Community 171 Jan 06, 2023
Complementary Patch for Weakly Supervised Semantic Segmentation, ICCV21 (poster)

CPN (ICCV2021) This is an implementation of Complementary Patch for Weakly Supervised Semantic Segmentation, which is accepted by ICCV2021 poster. Thi

Ferenas 20 Dec 12, 2022
EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks

EncT5 (Unofficial) Pytorch Implementation of EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks About Finetune T5 model for classification & r

Jangwon Park 34 Jan 01, 2023
[ICSE2020] MemLock: Memory Usage Guided Fuzzing

MemLock: Memory Usage Guided Fuzzing This repository provides the tool and the evaluation subjects for the paper "MemLock: Memory Usage Guided Fuzzing

Cheng Wen 54 Jan 07, 2023
Implementation of Memory-Compressed Attention, from the paper "Generating Wikipedia By Summarizing Long Sequences"

Memory Compressed Attention Implementation of the Self-Attention layer of the proposed Memory-Compressed Attention, in Pytorch. This repository offers

Phil Wang 47 Dec 23, 2022
This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers"

ICCV Workshop 2021 VTGAN This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers"

Sharif Amit Kamran 25 Dec 08, 2022
Multi-Glimpse Network With Python

Multi-Glimpse Network Multi-Glimpse Network: A Robust and Efficient Classification Architecture based on Recurrent Downsampled Attention arXiv Require

9 May 10, 2022
Differentiable Wavetable Synthesis

Differentiable Wavetable Synthesis

4 Feb 11, 2022
Code for Robust Contrastive Learning against Noisy Views

Robust Contrastive Learning against Noisy Views This repository provides a PyTorch implementation of the Robust InfoNCE loss proposed in paper Robust

Ching-Yao Chuang 53 Jan 08, 2023
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation (CVPR 2022)

CCAM (Unsupervised) Code repository for our paper "CCAM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localizati

Computer Vision Insitute, SZU 113 Dec 27, 2022
Code for DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents

DeepXML Code for DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents Architectures and algorithms DeepXML supports

Extreme Classification 49 Nov 06, 2022
An example to implement a new backbone with OpenMMLab framework.

Backbone example on OpenMMLab framework English | 简体中文 Introduction This is an template repo about how to use OpenMMLab framework to develop a new bac

Ma Zerun 22 Dec 29, 2022
Res2Net for Instance segmentation and Object detection using MaskRCNN

Res2Net for Instance segmentation and Object detection using MaskRCNN Since the MaskRCNN-benchmark of facebook is deprecated, we suggest to use our mm

Res2Net Applications 55 Oct 30, 2022