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

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.

An Approach to Explore Logistic Regression Models

User-centered Regression An Approach to Explore Logistic Regression Models This tool applies the potential of Attribute-RadViz in identifying correlat

0 Nov 12, 2021
ALBERT-pytorch-implementation - ALBERT pytorch implementation

ALBERT-pytorch-implementation developing... 모델의 개념이해를 돕기 위한 구현물로 현재 변수명을 상세히 적었고

BG Kim 3 Oct 06, 2022
A simple python program that can be used to implement user authentication tokens into your program...

token-generator A simple python module that can be used by developers to implement user authentication tokens into your program... code examples creat

octo 6 Apr 18, 2022
Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study

Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study Supplementary Materials for Kentaro Matsuura, Junya Honda, Imad

Kentaro Matsuura 4 Nov 01, 2022
Towards Multi-Camera 3D Human Pose Estimation in Wild Environment

PanopticStudio Toolbox This repository has a toolbox to download, process, and visualize the Panoptic Studio (Panoptic) data. Note: Sep-21-2020: Curre

335 Jan 09, 2023
UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation

UNION Automatic Evaluation Metric described in the paper UNION: An UNreferenced MetrIc for Evaluating Open-eNded Story Generation (EMNLP 2020). Please

50 Dec 30, 2022
Linear image-to-image translation

Linear (Un)supervised Image-to-Image Translation Examples for linear orthogonal transformations in PCA domain, learned without pairing supervision. Tr

Eitan Richardson 40 Aug 31, 2022
This is a simple backtesting framework to help you test your crypto currency trading. It includes a way to download and store historical crypto data and to execute a trading strategy.

You can use this simple crypto backtesting script to ensure your trading strategy is successful Minimal setup required and works well with static TP a

Andrei 154 Sep 12, 2022
NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.

NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.

100 Sep 28, 2022
Code and data form the paper BERT Got a Date: Introducing Transformers to Temporal Tagging

BERT Got a Date: Introducing Transformers to Temporal Tagging Satya Almasian*, Dennis Aumiller*, and Michael Gertz Heidelberg University Contact us vi

54 Dec 04, 2022
Neural Contours: Learning to Draw Lines from 3D Shapes (CVPR2020)

Neural Contours: Learning to Draw Lines from 3D Shapes This repository contains the PyTorch implementation for CVPR 2020 Paper "Neural Contours: Learn

93 Dec 16, 2022
WRENCH: Weak supeRvision bENCHmark

🔧 What is it? Wrench is a benchmark platform containing diverse weak supervision tasks. It also provides a common and easy framework for development

Jieyu Zhang 176 Dec 28, 2022
Weakly supervised medical named entity classification

Trove Trove is a research framework for building weakly supervised (bio)medical named entity recognition (NER) and other entity attribute classifiers

60 Nov 18, 2022
Model of an AI powered sign language interpreter.

TEXT AND SPEECH TO SIGN LANGUAGE. A web application which takes in text or live audio speech recording as input, converts and displays the relevant Si

Mark Gatere 4 Mar 30, 2022
10th place solution for Google Smartphone Decimeter Challenge at kaggle.

Under refactoring 10th place solution for Google Smartphone Decimeter Challenge at kaggle. Google Smartphone Decimeter Challenge Global Navigation Sat

12 Oct 25, 2022
[ACM MM 2021] Joint Implicit Image Function for Guided Depth Super-Resolution

Joint Implicit Image Function for Guided Depth Super-Resolution This repository contains the code for: Joint Implicit Image Function for Guided Depth

hawkey 78 Dec 27, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time

DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time Introduction This is official implementation for DR-GAN (IEEE TCS

Kang Liao 18 Dec 23, 2022
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

deepface Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid

Sefik Ilkin Serengil 5.2k Jan 02, 2023
Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations

NANSY: Unofficial Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations Notice Papers' D

Dongho Choi 최동호 104 Dec 23, 2022