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.

MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens

MSG-Transformer Official implementation of the paper MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens, by Jiemin

Hust Visual Learning Team 68 Nov 16, 2022
“英特尔创新大师杯”深度学习挑战赛 赛道3:CCKS2021中文NLP地址相关性任务

基于 bert4keras 的一个baseline 不作任何 数据trick 单模 线上 最高可到 0.7891 # 基础 版 train.py 0.7769 # transformer 各层 cls concat 明神的trick https://xv44586.git

孙永松 7 Dec 28, 2021
HTSeq is a Python library to facilitate processing and analysis of data from high-throughput sequencing (HTS) experiments.

HTSeq DEVS: https://github.com/htseq/htseq DOCS: https://htseq.readthedocs.io A Python library to facilitate programmatic analysis of data from high-t

HTSeq 57 Dec 20, 2022
Code for "Solving Graph-based Public Good Games with Tree Search and Imitation Learning"

Code for "Solving Graph-based Public Good Games with Tree Search and Imitation Learning" This is the code for the paper Solving Graph-based Public Goo

Victor-Alexandru Darvariu 3 Dec 05, 2022
Dynamic View Synthesis from Dynamic Monocular Video

Dynamic View Synthesis from Dynamic Monocular Video Project Website | Video | Paper Dynamic View Synthesis from Dynamic Monocular Video Chen Gao, Ayus

Chen Gao 139 Dec 28, 2022
Code and training data for our ECCV 2016 paper on Unsupervised Learning

Shuffle and Learn (Shuffle Tuple) Created by Ishan Misra Based on the ECCV 2016 Paper - "Shuffle and Learn: Unsupervised Learning using Temporal Order

Ishan Misra 44 Dec 08, 2021
Diagnostic tests for linguistic capacities in language models

LM diagnostics This repository contains the diagnostic datasets and experimental code for What BERT is not: Lessons from a new suite of psycholinguist

61 Jan 02, 2023
Implementation of ProteinBERT in Pytorch

ProteinBERT - Pytorch (wip) Implementation of ProteinBERT in Pytorch. Original Repository Install $ pip install protein-bert-pytorch Usage import torc

Phil Wang 92 Dec 25, 2022
Development kit for MIT Scene Parsing Benchmark

Development Kit for MIT Scene Parsing Benchmark [NEW!] Our PyTorch implementation is released in the following repository: https://github.com/hangzhao

MIT CSAIL Computer Vision 424 Dec 01, 2022
[ICCV 2021 Oral] NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo

NerfingMVS Project Page | Paper | Video | Data NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo Yi Wei, Shaohui

Yi Wei 369 Dec 24, 2022
Tools for the Cleveland State Human Motion and Control Lab

Introduction This is a collection of tools that are helpful for gait analysis. Some are specific to the needs of the Human Motion and Control Lab at C

CSU Human Motion and Control Lab 88 Dec 16, 2022
Code for "Continuous-Time Meta-Learning with Forward Mode Differentiation" (ICLR 2022)

Continuous-Time Meta-Learning with Forward Mode Differentiation ICLR 2022 (Spotlight) - Installation - Example - Citation This repository contains the

Tristan Deleu 25 Oct 20, 2022
The MLOps platform for innovators 🚀

​ DS2.ai is an integrated AI operation solution that supports all stages from custom AI development to deployment. It is an AI-specialized platform service that collects data, builds a training datas

9 Jan 03, 2023
[Arxiv preprint] Causality-inspired Single-source Domain Generalization for Medical Image Segmentation (code&data-processing pipeline)

Causality-inspired Single-source Domain Generalization for Medical Image Segmentation Arxiv preprint Repository under construction. Might still be bug

Cheng 31 Dec 27, 2022
A JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short.

BraVe This is a JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short. The model provided in this package wa

DeepMind 44 Nov 20, 2022
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation

HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation Official PyTroch implementation of HPRNet. HPRNet: Hierarchical Point Regre

Nermin Samet 53 Dec 04, 2022
Object detection and instance segmentation toolkit based on PaddlePaddle.

Object detection and instance segmentation toolkit based on PaddlePaddle.

9.3k Jan 02, 2023
Confident Semantic Ranking Loss for Part Parsing

Confident Semantic Ranking Loss for Part Parsing

Jiachen Xu 5 Oct 22, 2022
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework

NLP From Scratch Without Large-Scale Pretraining This repository contains the code, pre-trained model checkpoints and curated datasets for our paper:

Xingcheng Yao 224 Dec 08, 2022
The code for paper "Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation" which is accepted by AAAI 2022

Contrastive Spatio Temporal Pretext Learning for Self-supervised Video Representation (AAAI 2022) The code for paper "Contrastive Spatio-Temporal Pret

8 Jun 30, 2022