  Sound event detection systems typically consist of two stages: extracting
hand-crafted features from the raw audio waveform, and learning a mapping
between these features and the target sound events using a classifier.
Recently, the focus of sound event detection research has been mostly shifted
to the latter stage using standard features such as mel spectrogram as the
input for classifiers such as deep neural networks. In this work, we utilize
end-to-end approach and propose to combine these two stages in a single deep
neural network classifier. The feature extraction over the raw waveform is
conducted by a feedforward layer block, whose parameters are initialized to
extract the time-frequency representations. The feature extraction parameters
are updated during training, resulting with a representation that is optimized
for the specific task. This feature extraction block is followed by (and
jointly trained with) a convolutional recurrent network, which has recently
given state-of-the-art results in many sound recognition tasks. The proposed
system does not outperform a convolutional recurrent network with fixed
hand-crafted features. The final magnitude spectrum characteristics of the
feature extraction block parameters indicate that the most relevant information
for the given task is contained in 0 - 3 kHz frequency range, and this is also
supported by the empirical results on the SED performance.

Sound;Machine Learning;Audio and Speech Processing;Machine Learning