  Environmental sound detection is a challenging application of machine
learning because of the noisy nature of the signal, and the small amount of
(labeled) data that is typically available. This work thus presents a
comparison of several state-of-the-art Deep Learning models on the IEEE
challenge on Detection and Classification of Acoustic Scenes and Events (DCASE)
2016 challenge task and data, classifying sounds into one of fifteen common
indoor and outdoor acoustic scenes, such as bus, cafe, car, city center, forest
path, library, train, etc. In total, 13 hours of stereo audio recordings are
available, making this one of the largest datasets available. We perform
experiments on six sets of features, including standard Mel-frequency cepstral
coefficients (MFCC), Binaural MFCC, log Mel-spectrum and two different large-
scale temporal pooling features extracted using OpenSMILE. On these features,
we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN),
Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and
i-vector. Using the late-fusion approach, we improve the performance of the
baseline 72.5% by 15.6% in 4-fold Cross Validation (CV) avg. accuracy and 11%
in test accuracy, which matches the best result of the DCASE 2016 challenge.
With large feature sets, deep neural network models out- perform traditional
methods and achieve the best performance among all the studied methods.
Consistent with other work, the best performing single model is the
non-temporal DNN model, which we take as evidence that sounds in the DCASE
challenge do not exhibit strong temporal dynamics.
