  Speech, Music and Noise classification/segmentation is an important
preprocessing step for audio processing/indexing. To this end, we propose a
novel 1D Convolutional Neural Network (CNN) - SwishNet. It is a fast and
lightweight architecture that operates on MFCC features which is suitable to be
added to the front-end of an audio processing pipeline. We showed that the
performance of our network can be improved by distilling knowledge from a 2D
CNN, pretrained on ImageNet. We investigated the performance of our network on
the MUSAN corpus - an openly available comprehensive collection of noise, music
and speech samples, suitable for deep learning. The proposed network achieved
high overall accuracy in clip (length of 0.5-2s) classification (>97% accuracy)
and frame-wise segmentation (>93% accuracy) tasks with even higher accuracy
(>99%) in speech/non-speech discrimination task. To verify the robustness of
our model, we trained it on MUSAN and evaluated it on a different corpus -
GTZAN and found good accuracy with very little fine-tuning. We also
demonstrated that our model is fast on both CPU and GPU, consumes a low amount
of memory and is suitable for implementation in embedded systems.
