• Corpus ID: 239016457

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

@article{Hsu2021DeepLB,
  title={Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features},
  author={Wei-Han Hsu and Bo-Yu Chen and Yi-Hsuan Yang},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.08862}
}
Along with the evolution of music technology, a large number of styles, or “subgenres,” of Electronic Dance Music (EDM) have emerged in recent years. While the classification task of distinguishing between EDM and non-EDM has been often studied in the context of music genre classification, little work has been done on the more challenging EDM subgenre classification. The state-of-art model is based on extremely randomized trees and could be improved by deep learning methods. In this paper, we… 

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References

SHOWING 1-10 OF 31 REFERENCES
Evaluation of CNN-based Automatic Music Tagging Models
TLDR
A consistent evaluation of different music tagging models on three datasets is conducted and reference results using common evaluation metrics are provided and all the models are evaluated with perturbed inputs to investigate the generalization capabilities concerning time stretch, pitch shift, dynamic range compression, and addition of white noise.
Intelligent classification of electronic music
TLDR
This work describes an approach to classify four major sub-genres of EDM using artificial neural networks, and employs the RELIEFF feature selection algorithm to reduce the number of features and improve the results.
Automatic music tagging with Harmonic CNN
Feature design was one of the main focuses in early stages of music informatics research (MIR), where such features were later used as input to machine learning models to, e.g., bridge the semantic
Timbre analysis of music audio signals with convolutional neural networks
TLDR
One of the main goals of this work is to design efficient CNN architectures — what reduces the risk of these models to over-fit, since CNNs' number of parameters is minimized.
Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach
TLDR
Over the last decade, music-streaming services have grown dramatically and giant technology companies such as Apple, Google, and Amazon have also been strengthening their music service platforms, providing listeners with a new and easily accessible way to listen to music.
Pop Music Highlighter: Marking the Emotion Keypoints
TLDR
An attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, for Pop songs is introduced and a new architecture that does not need any recurrent layers is experiment with, making the training process faster.
Automatic subgenre classification in an electronic dance music taxonomy
TLDR
This work illustrates the main challenges that EDM poses to automatic classification and provides insights into where are the limits of this approach.
musicnn: Pre-trained convolutional neural networks for music audio tagging
TLDR
The musicnn library contains a set of pre-trained musically motivated convolutional neural networks for music audio tagging, which can be used as out-of-the-box music audio taggers, as music feature extractors, or as pre- trained models for transfer learning.
Toward Interpretable Music Tagging with Self-Attention
TLDR
Compared to conventional approaches using fully convolutional or recurrent neural networks, the proposed self-attention based deep sequence model for music tagging is more interpretable while reporting competitive results.
Event Localization in Music Auto-tagging
TLDR
This paper proposes a convolutional neural network architecture that is able to make accurate frame-level predictions of tags in unseen music clips by using only clip-level annotations in the training phase, and presents qualitative analyses showing the model can indeed learn certain characteristics of music tags.
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