Paper: http://ceur-ws.org/Vol-2283/MediaEval_18_paper_30.pdf Youtube: https://youtu.be/lrLf8M-aXlY Hendrik Schreiber, MediaEval 2018 AcousticBrainz Genre Task: A CNN Baseline Relying on Mel-Features. Proc. of MediaEval 2018, 29-31 October 2018, Sophia Antipolis, France. Abstract: These working notes describe a relatively simple baseline for the MediaEval 2018 AcousticBrainz Genre Task. As classifier it uses a fully convolutional neural network (CNN) based on only the low-level AcousticBrainz melband features as input. Presented by Hendrik Schreiber

1. MediaEval 2018 AcousticBrainz Genre Task:
A CNN Baseline Relying on Mel-Features
Hendrik Schreiber
(tagtraum industries inc./International Audio Labs Erlangen)

2. Features
● Mel-features have been used extensively in the
literature
● Baseline idea: Use only lowlevel Mel-features
(mean, max, min, etc.)
● 40 bands each for 9 different global statistics
● = 360 input features

3. Neural Network
● Mel-features have a spatial
relationship
● Convolutional Neural Network
(CNN)
● 40 bands with 9 channels

4. Prediction
● Choose threshold to maximize F-Score on validation set (plug-in
rule-approach)

5. Subtask 1 vs Subtask 2
● Subtask 2: normalize label names (“childrens” == “children’s”) for training,
revert for prediction
● Train on the union of all data instead of just one dataset
● No further differences

6. Results
● Similar to 2nd ranked submission in 2017
● Results for subtask 2 worse than for subtask 1
● Shown that relatively few Mel-features can lead to reasonable results

7. Thank you!