Spectral vs. spectro-temporal features for acoustic event detection

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Detection and Classification of Acoustic Scenes and Events (2015, methodology): “The majority of work in event detection treats the sound signal as monophonic, with only one event detectable at a time [19], [20].”
A Dataset and Taxonomy for Urban Sound Research (2014, background): “Intuitively, one would expect the baseline algorithm to do better on slices where there is less background interference, especially since MFCCs are known to be sensitive to noise [6].”
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification (2016, background): “convolutional kernels (filters) with a small receptive field, the network should, in principle, be able to successfully learn and later identify spectro-temporal patterns that are representative of different sound classes even if part of the sound is masked (in time/frequency) by other sources (nois”