Tutorial on Tempo, Beat and Downbeat estimation

Last update: Nov 6, 2022

Related tags

Miscellaneous python tutorial book beat tempo mir downbeat

Overview

Tempo, Beat and Downbeat Estimation

By Matthew E. P. Davies, Sebastian Böck and Magdalena Fuentes

Resources and Jupyter Book for the ISMIR 2021 tutorial on Tempo, Beat and Downbeat estimation.

To jump directly to the hands on part:

To go directly to the book: https://tempobeatdownbeat.github.io/tutorial

Install

We recommended you do this within a virtual environment:

conda create -n beat python=3.8
conda activate beat
git clone [email protected]:TempoBeatDownbeat/tutorial.git
cd rhythm_tutorial/
pip install -e .

Building the book

cd rhythm_tutorial/
jupyter-book clean book/  # to remove any existing builds
jupyter-book build book/

A fully-rendered HTML version of the book will be built in book/_build/html/.

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Comments

Issue on page /ch3_going_deep/postprocessing.html

In the Hidden Markov Model section, the objective of inference is written as: Since y represents the hidden states, shouldn't the correct objective be argmax_y rather than argmax_x?

opened by Xiao-Ming 0
Add related work to table

Hey, Here I am opening this issue to request to include the following works in the related works table if they fit into the category!

#1:
M. Heydari and Z. Duan, “Don’t Look Back: An online beat tracking method using RNN and enhanced particle filtering,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2021. Don't look back uses pre-trained RNN's from Madmom and a particle filtering inference for online beat tracking https://arxiv.org/abs/2011.02619

#2: M. Heydari, F. Cwitkowitz, and Z. Duan, “BeatNet: CRNN and particle filtering for online joint beat downbeat and meter tracking,” in Proc. of the 22nd Intl. Conf. on Music Information Retrieval (ISMIR), 2021. The BeatNet uses CRNN and particle filtering for online music joint beat, downbeat, and meter tracking. https://arxiv.org/abs/2108.03576 https://github.com/mjhydri/BeatNet

#3:
M. Heydari, M. McCallum, A. Ehmann, and Z. Duan, "A Novel 1D State Space for Efficient Music Rhythmic Analysis", In Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2022. #(Recently submitted) This paper proposes a novel 1D state space and a jump-reward inference model for online music joint beat, downbeat, tempo, and meter tracking. https://arxiv.org/abs/2111.00704 https://github.com/mjhydri/1D-StateSpace

Many thanks!

opened by mjhydri 1

Tutorial on Tempo, Beat and Downbeat estimation

Related tags

Overview

Tempo, Beat and Downbeat Estimation

Install

Building the book

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Comments

Issue on page /ch3_going_deep/postprocessing.html

Add related work to table

Owner

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