Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features"

Related tags

Deep LearningEDM-subgenre-classifier
Overview

EDM-subgenre-classifier

This repository contains the code for "Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features" arXiv:2110.08862, 2021. Wei-Han Hsu, Bo-Yu Chen, Yi-Hsuan Yang

Prerequisites

  • Python == 3.6.13
  • librosa == 0.8.1
  • torch == 1.3.0
  • numpy == 1.16.0
  • pandas == 1.1.2

Running the application

You could run

pip3 install -r requirements.txt

to make the part of installing dependencies.

Then run

python3 main.py

could predict the song's genre directly.

Here are the steps to run the code:

  • Step 1 : Preparing the audio (mp3, wav) and put it under "./data/audio/"
  • Step 2 : Extracting the feature, and the feature will under the ./data/{feature_folder}" (feature_folder : mel-spectrogram, fourier-tempogram and auto-tempogram)
  • Step 3 : Classifying the audio by feature
  • Step 4 : The result will be in the "./result.csv"
  • All the "step" could complete by main.py

We only put the "late-fusion model" in this repo.

Note

This project was revised from sota-music-tagging-models. Many thanks for all authors of the paper ("Evaluation of CNN-based Automatic Music Tagging Models", SMC 2020)

License


MIT License

Copyright (c) 2020 Music Technology Group, Universitat Pompeu Fabra. Code developed by Minz Won.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Contact

Please feel free to contact Wei-Han Hsu if you have any questions.

Owner
GitHub Repository
Official implementation of the paper Visual Parser: Representing Part-whole Hierarchies with Transformers

Visual Parser (ViP) This is the official implementation of the paper Visual Parser: Representing Part-whole Hierarchies with Transformers. Key Feature

Shuyang Sun 117 Dec 11, 2022
Revisting Open World Object Detection

Revisting Open World Object Detection Installation See INSTALL.md. Dataset Our new data division is based on COCO2017. We divide the training set into

58 Dec 23, 2022
Readings for "A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction."

Polypharmacy - DDI - Synergy Survey The Survey Paper This repository accompanies our survey paper A Unified View of Relational Deep Learning for Polyp

AstraZeneca 79 Jan 05, 2023
Benchmark for the generalization of 3D machine learning models across different remeshing/samplings of a surface.

Discretization Robust Correspondence Benchmark One challenge of machine learning on 3D surfaces is that there are many different representations/sampl

Nicholas Sharp 10 Sep 30, 2022
Code repository accompanying the paper "On Adversarial Robustness: A Neural Architecture Search perspective"

On Adversarial Robustness: A Neural Architecture Search perspective Preparation: Clone the repository: https://github.com/tdchaitanya/nas-robustness.g

Chaitanya Devaguptapu 4 Nov 10, 2022
An open-source, low-cost, image-based weed detection device for fallow scenarios.

Welcome to the OpenWeedLocator (OWL) project, an opensource hardware and software green-on-brown weed detector that uses entirely off-the-shelf compon

Guy Coleman 145 Jan 05, 2023
Snscrape-jsonl-urls-extractor - Extracts urls from jsonl produced by snscrape

snscrape-jsonl-urls-extractor extracts urls from jsonl produced by snscrape Usag

1 Feb 26, 2022
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English ⚖️ 🏆 🧑‍🎓 👩‍⚖️ Dataset Summary Inspired by the recent widespread use of th

95 Dec 08, 2022
Locally Differentially Private Distributed Deep Learning via Knowledge Distillation (LDP-DL)

Locally Differentially Private Distributed Deep Learning via Knowledge Distillation (LDP-DL) A preprint version of our paper: Link here This is a samp

Di Zhuang 3 Jan 08, 2023
Interactive Image Generation via Generative Adversarial Networks

iGAN: Interactive Image Generation via Generative Adversarial Networks Project | Youtube | Paper Recent projects: [pix2pix]: Torch implementation for

Jun-Yan Zhu 3.9k Dec 23, 2022
The modify PyTorch version of Siam-trackers which are speed-up by TensorRT.

SiamTracker-with-TensorRT The modify PyTorch version of Siam-trackers which are speed-up by TensorRT or ONNX. [Updating...] Examples demonstrating how

9 Dec 13, 2022
DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation

DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation This project hosts the code for implementing the DCT-MASK algorithms

Alibaba Cloud 57 Nov 27, 2022
OpenMMLab Image and Video Editing Toolbox

Introduction MMEditing is an open source image and video editing toolbox based on PyTorch. It is a part of the OpenMMLab project. The master branch wo

OpenMMLab 3.9k Jan 04, 2023
PyTorch Implementation of Temporal Output Discrepancy for Active Learning, ICCV 2021

Temporal Output Discrepancy for Active Learning PyTorch implementation of Semi-Supervised Active Learning with Temporal Output Discrepancy, ICCV 2021.

Siyu Huang 33 Dec 06, 2022
Anderson Acceleration for Deep Learning

Anderson Accelerated Deep Learning (AADL) AADL is a Python package that implements the Anderson acceleration to speed-up the training of deep learning

Oak Ridge National Laboratory 7 Nov 24, 2022
Code for the bachelors-thesis flaky fault localization

Flaky_Fault_Localization Scripts for the Bachelors-Thesis: "Flaky Fault Localization" by Christian Kasberger. The thesis examines the usefulness of sp

Christian Kasberger 1 Oct 26, 2021
Data reduction pipeline for KOALA on the AAT.

KOALA KOALA, the Kilofibre Optical AAT Lenslet Array, is a wide-field, high efficiency, integral field unit used by the AAOmega spectrograph on the 3.

4 Sep 26, 2022
Code for the paper "Balancing Training for Multilingual Neural Machine Translation, ACL 2020"

Balancing Training for Multilingual Neural Machine Translation Implementation of the paper Balancing Training for Multilingual Neural Machine Translat

Xinyi Wang 21 May 18, 2022
Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

LUPerson-NL Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL) The repository is for our CVPR2022 paper Large-Scale

43 Dec 26, 2022
A Python package for causal inference using Synthetic Controls

Synthetic Control Methods A Python package for causal inference using synthetic controls This Python package implements a class of approaches to estim

Oscar Engelbrektson 107 Dec 28, 2022