Music auto-tagger using keras
- use keras == 1.0.6 for
MusicTaggerCNN. (will fix soon)
- use keras > 1.0.6 for
- You need
- To use your own audio file, you need
- To use your own audio file, you need
- The input data shape is
(None, channel, height, width), i.e. following theano convention. If you're using tensorflow as your backend, you should check out
image_dim_orderingis set to
- audio_processor.py: compute mel-spectrogram using librosa
- Under data/,
- 5-layer 2D Convolutions
- num_parameter: 865,950
- AUC score of 0.8654
- WARNING with keras >1.0.6, this model does not work properly. Please use MusicTaggerCRNN until it is updated! (FYI: with 3M parameter, a deeper ConvNet showed 0.8595 AUC.)
- 4-layer 2D Convolutions + 2 GRU
- num_parameter: 396,786
- AUC score: 0.8662
How was it trained?
- Using 29.1s music files in Million Song Dataset
- split setting: A repo for split setting for an identical setting.
- See papers
- The tags are...
['rock', 'pop', 'alternative', 'indie', 'electronic', 'female vocalists', 'dance', '00s', 'alternative rock', 'jazz', 'beautiful', 'metal', 'chillout', 'male vocalists', 'classic rock', 'soul', 'indie rock', 'Mellow', 'electronica', '80s', 'folk', '90s', 'chill', 'instrumental', 'punk', 'oldies', 'blues', 'hard rock', 'ambient', 'acoustic', 'experimental', 'female vocalist', 'guitar', 'Hip-Hop', '70s', 'party', 'country', 'easy listening', 'sexy', 'catchy', 'funk', 'electro' ,'heavy metal', 'Progressive rock', '60s', 'rnb', 'indie pop', 'sad', 'House', 'happy']
Which is the better predictor?
MusicTaggerCNNis faster than
- Prediction: They are more or less the same.
- Memory Usage:
MusicTaggerCRNNhave smaller number of trainable parameters. Actually you can even decreases the number of feature maps. The
MusicTaggerCRNNstill works quite well in the case - i.e., the current setting is a little bit rich (or redundant). With
MusicTaggerCNN, you will see the performance decrease if you reduce down the parameters.
Therefore, if you just wanna use the pre-trained weights, use
MusicTaggerCNN. If you wanna train by yourself, it's up to you. I would use
MusicTaggerCRNN after downsizing it to, like, 0.2M parameters (then the training time would be similar to
MusicTaggerCNN) in general. To reduce the size, change number of feature maps of convolution layers.
Which is the better feature extractor?
include_top=False, you can get 256-dim (
MusicTaggerCNN) or 32-dim (
MusicTaggerCRNN) feature representation.
In general, I would recommend to use
MusicTaggerCRNN and 32-dim feature as for predicting 50 tags, 256 features actually sound bit too large. I haven't looked into 256-dim feature but only 32-dim features. I thought of using PCA to reduce the dimension more, but ended up not applying it because
mean(abs(recovered - original) / original) are
.12 (dim: 32->16),
.05 (dim: 32->24) - which don't seem good enough.
Probably the 256-dim features are redundant (which then you can reduce them down effectively with PCA), or they just include more information than 32-dim ones (e.g., features in different hierarchical levels). If the dimension size would not matter, it's worth choosing 256-dim ones.
$ python example_tagging.py $ python example_feat_extract.py
data/bensound-cute.mp3 [('jazz', '0.444'), ('instrumental', '0.151'), ('folk', '0.103'), ('Hip-Hop', '0.103'), ('ambient', '0.077')] [('guitar', '0.068'), ('rock', '0.058'), ('acoustic', '0.054'), ('experimental', '0.051'), ('electronic', '0.042')] data/bensound-actionable.mp3 [('jazz', '0.416'), ('instrumental', '0.181'), ('Hip-Hop', '0.085'), ('folk', '0.085'), ('rock', '0.081')] [('ambient', '0.068'), ('guitar', '0.062'), ('Progressive rock', '0.048'), ('experimental', '0.046'), ('acoustic', '0.046')] data/bensound-dubstep.mp3 [('Hip-Hop', '0.245'), ('rock', '0.183'), ('alternative', '0.081'), ('electronic', '0.076'), ('alternative rock', '0.053')] [('metal', '0.051'), ('indie', '0.028'), ('instrumental', '0.027'), ('electronica', '0.024'), ('hard rock', '0.023')] data/bensound-thejazzpiano.mp3 [('jazz', '0.299'), ('instrumental', '0.174'), ('electronic', '0.089'), ('ambient', '0.061'), ('chillout', '0.052')] [('rock', '0.044'), ('guitar', '0.044'), ('funk', '0.033'), ('chill', '0.032'), ('Progressive rock', '0.029')]
- More info - CNN:
- More info - RNN:
Reproduce the experiment
- A repo for split setting for an identical setting of experiments in two papers.
- Audio file: find someone around you who happened to have the preview clips. or you have to crawl the files. I would recommend you to crawl your colleagues...
- Convnet: Automatic Tagging using Deep Convolutional Neural Networks, Keunwoo Choi, George Fazekas, Mark Sandler 17th International Society for Music Information Retrieval Conference, New York, USA, 2016
ConvRNN : Convolutional Recurrent Neural Networks for Music Classification, Keunwoo Choi, George Fazekas, Mark Sandler, Kyunghyun Cho, arXiv:1609.04243, 2016
Test music items are from http://www.bensound.com.