Challenge info: http:https://machine-listening.eecs.qmul.ac.uk/bird-audio-detection-challenge/

The task is to design a system that, given a short audio recording, returns a binary decision for the presence/absence of bird sound (bird sound of any kind).

Pretty straightforward implementation of convolutional detectors for this task, nothing too fancy. Data augmentation done with:

• noise corruption of training files,
• averaging of two training spectrograms at random,
• time-shifts of whole recordings,
• permutations of spectrogram segments.

Final submission consists of averaged predictions from 6 nets:

High resolution spectrogram convnet:

• trained on whole dataset,
• 5 conv/pooling LeakyReLU layers with batch normalization and delta channel,
• global max pooling before output.

Same as run-A, different random seed.

Same as run-A, but low resolution (downsampled).

Same as run-A_ds, different random seed.

Low resolution spectrogram convnet.

When compared to runs A_ds/B_ds:

• less filters,
• no L2 regularization,
• more epochs,
• faster learning rate decay,
• less dropout.

Originally used shuffle_spec() with 5 segments (spec length of 475) and less noise augmentations to generate the predictions.

High resolution spectrogram convnet.

When compared to runs A/B:

• more filters,
• lower regularization,
• lower learning rate,
• more epochs.