Before revision:  We show that these modifications consistently improve sound event classification in all cases considered , without adding any (or adding very few) trainable parameters, which makes them an appealing alternative to conventional pooling layers.
After revision:  We show that these modifications consistently improve sound event classification in all cases considered . We also demonstrate empirically that the proposed pooling methods increase shift invariance in the network, making it more robust against time/frequency shifts in input spectrograms. This is achieved by adding a negligible amount of trainable parameters, which makes them an appealing alternative to conventional pooling layers.