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FMA_convnet_features

Keunwoo Choi, May 2017

A repo to host convnet features for FMA. Wanted to do more experiment than baseline method, but don't think I have time to do so, so just releasing it.

What is convnet feature?

A set of features that is computed from transfer learning repo

What is FMA?

Free Music Archieve dataset: A large collection of audio and its genre annotation released in 2017.

How good is the feature?

How to use?

# Load the features
import numpy as np
feat1 = np.load('fma_large_layer1.npy')
feat2 = np.load('fma_large_layer2.npy')
feat3 = np.load('fma_large_layer3.npy')
feat4 = np.load('fma_large_layer4.npy')
feat5 = np.load('fma_large_layer5.npy')

# concatenate the features
features = np.concatenate((feat1, feat2, feat3, feat4, feat5), axis=1)
features.shape
# (106574, 160)
# This is matched to the order of metadata.csv provided in FMA.
# Now use it for your task!

Classification results (Compare these with the results from provided baselines)

result

T-SNE

on FMA-'small'

tsne on small

on FMA-'medium'

tsne on medium

Details