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.

A dataset for online Arabic calligraphy

Calliar Calliar is a dataset for Arabic calligraphy. The dataset consists of 2500 json files that contain strokes manually annotated for Arabic callig

ARBML 114 Dec 28, 2022
deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

63 Oct 17, 2022
Header-only library for using Keras models in C++.

frugally-deep Use Keras models in C++ with ease Table of contents Introduction Usage Performance Requirements and Installation FAQ Introduction Would

Tobias Hermann 927 Jan 05, 2023
OBG-FCN - implementation of 'Object Boundary Guided Semantic Segmentation'

OBG-FCN This repository is to reproduce the implementation of 'Object Boundary Guided Semantic Segmentation' in http://arxiv.org/abs/1603.09742 Object

Jiu XU 3 Mar 11, 2019
FFCV: Fast Forward Computer Vision (and other ML workloads!)

Fast Forward Computer Vision: train models at a fraction of the cost with accele

FFCV 2.3k Jan 03, 2023
CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery

CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery This paper (CoANet) has been published in IEEE TIP 2021. This code i

Jie Mei 53 Dec 03, 2022
Code for the TPAMI paper: "Syntax Customized Video Captioning by Imitating Exemplar Sentences"

Syntax-Customized-Video-Captioning Code for the TPAMI paper: "Syntax Customized Video Captioning by Imitating Exemplar Sentences". This is my second w

3 Dec 05, 2022
Generalized Random Forests

generalized random forests A pluggable package for forest-based statistical estimation and inference. GRF currently provides non-parametric methods fo

GRF Labs 781 Dec 25, 2022
Anomaly detection in multi-agent trajectories: Code for training, evaluation and the OpenAI highway simulation.

Anomaly Detection in Multi-Agent Trajectories for Automated Driving This is the official project page including the paper, code, simulation, baseline

12 Dec 02, 2022
Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Yihong Sun 12 Nov 15, 2022
List of all dependencies affected by node-ipc malicious commit

node-ipc-dependencies-list List of all dependencies affected by node-ipc malicious commit as of 17/3/2022 - 19/3/2022 (timestamp) Please improve upon

99 Oct 15, 2022
Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation

Kim Seonghyeon 502 Jan 03, 2023
Reinforcement learning library in JAX.

Reinforcement learning library in JAX.

Yicheng Luo 96 Oct 30, 2022
Implementation of Sequence Generative Adversarial Nets with Policy Gradient

SeqGAN Requirements: Tensorflow r1.0.1 Python 2.7 CUDA 7.5+ (For GPU) Introduction Apply Generative Adversarial Nets to generating sequences of discre

Lantao Yu 2k Dec 29, 2022
Code for technical report "An Improved Baseline for Sentence-level Relation Extraction".

RE_improved_baseline Code for technical report "An Improved Baseline for Sentence-level Relation Extraction". Requirements torch = 1.8.1 transformers

Wenxuan Zhou 74 Nov 29, 2022
Bridging Vision and Language Model

BriVL BriVL (Bridging Vision and Language Model) 是首个中文通用图文多模态大规模预训练模型。BriVL模型在图文检索任务上有着优异的效果,超过了同期其他常见的多模态预训练模型(例如UNITER、CLIP)。 BriVL论文:WenLan: Bridgi

235 Dec 27, 2022
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Python Streaming Anomaly Detection (PySAD) PySAD is an open-source python framework for anomaly detection on streaming multivariate data. Documentatio

Selim Firat Yilmaz 181 Dec 18, 2022
Learning cell communication from spatial graphs of cells

ncem Features Repository for the manuscript Fischer, D. S., Schaar, A. C. and Theis, F. Learning cell communication from spatial graphs of cells. 2021

Theis Lab 77 Dec 30, 2022
TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? Source: Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize

Aritra Roy Gosthipaty 23 Dec 24, 2022
clustering moroccan stocks time series data using k-means with dtw (dynamic time warping)

Moroccan Stocks Clustering Context Hey! we don't always have to forecast time series am I right ? We use k-means to cluster about 70 moroccan stock pr

Ayman Lafaz 7 Oct 18, 2022