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Speech, Music and Noise classification/segmentation is an importantpreprocessing step for audio processing/indexing. To this end, we propose anovel 1D Convolutional Neural Network (CNN) - SwishNet. It is a fast andlightweight architecture that operates on MFCC features which is suitable to beadded to the front-end of an audio processing pipeline. We showed that theperformance of our network can be improved by distilling knowledge from a 2DCNN, pretrained on ImageNet. We investigated the performance of our network onthe MUSAN corpus - an openly available comprehensive collection of noise, musicand speech samples, suitable for deep learning. The proposed network achievedhigh 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 ofour model, we trained it on MUSAN and evaluated it on a different corpus -GTZAN and found good accuracy with very little fine-tuning. We alsodemonstrated that our model is fast on both CPU and GPU, consumes a low amountof memory and is suitable for implementation in embedded systems.

SwishNet: A Fast Convolutional Neural Network for Speech, Music and Noise Classification and Segmentation