Convolutional Recurrent Neural Networks for Music Classification

Code

• github.com/keunwoochoi/icassp_2017
  OfficialIn papernone★ 0
• github.com/keunwoochoi/music-auto_tagging-keras
  tf★ 612
• github.com/JerryKwon/melon_plylst_continuation
  tf★ 1
• github.com/maysa96/music_tagger_mine2
  tf★ 0
• github.com/vvnkumar1965/chitti
  tf★ 0
• github.com/TejInaco/multimodalML
  none★ 0
• github.com/andresmanzalini/ClasificacionGeneroMusical_DL
  tf★ 0
• github.com/keunwoochoi/MSD_split_for_tagging
  none★ 0
• github.com/beerzyp/ECAC-Bimodal_audiovisual_recognition
  tf★ 0
• github.com/dbalaji5/audiotagging
  tf★ 0

Abstract

We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks for temporal summarisation of the extracted features. We compare CRNN with three CNN structures that have been used for music tagging while controlling the number of parameters with respect to their performance and training time per sample. Overall, we found that CRNNs show a strong performance with respect to the number of parameter and training time, indicating the effectiveness of its hybrid structure in music feature extraction and feature summarisation.

Tasks

Reproductions