Musical-Note-Classification

This repository contains the implementation for Homework 0 of the CommE5070 Deep Learning for Music Analysis and Generation course, Fall 2024, at National Taiwan University. For a detailed report, please refer to this slides.

Setup

To set up the virtual environment and install the required packages, use the following commands:

virtualenv --python=python3.10 deepmir_hw0
source deepmir_hw0/bin/activate
pip install -r requirements.txt

Data and Checkpoint Download

Dataset

To download the dataset, run the following script:

bash scripts/download_data.sh

Checkpoint

To download the pre-trained model checkpoints, use the command:

bash scripts/download_ckpt.sh

Tasks

Task 1: Visualize a Mel-Spectrogram

To generate and visualize a Mel-Spectrogram:

bash scripts/run_task1.sh

Task 2: Train a Traditional Machine Learning Model

To train a traditional machine learning model:

bash scripts/run_task2.sh

Task 3: Train a Deep Learning Model

• To train a deep learning model:

bash scripts/run_task3_train.sh

• To run inference with the trained model:

bash scripts/run_task3_infer.sh

Reproducing Task3 Result

• If you want to reproduce the inference result, you can run:

bash scripts/run_task3_reproduce.sh

Environment

We implemented the code on an environment running Ubuntu 22.04.1, utilizing a 12th Generation Intel(R) Core(TM) i7-12700 CPU, along with a single NVIDIA GeForce RTX 4090 GPU equipped with 24 GB of dedicated memory.

Citation

If you use this code, please cite the following:

@misc{musical_note_classification_2024,
    title  = {Music Note Classification},
    author = {Jia-Wei Liao},
    url    = {https://github.com/jwliao1209/Musical-Note-Classification},
    year   = {2024}
}