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Automatic classification of sound commands is becoming increasinglyimportant, especially for mobile and embedded devices. Many of these devicescontain both cameras and microphones, and companies that develop them wouldlike to use the same technology for both of these classification tasks. One wayof achieving this is to represent sound commands as images, and useconvolutional neural networks when classifying images as well as sounds. Inthis paper we consider several approaches to the problem of soundclassification that we applied in TensorFlow Speech Recognition Challengeorganized by Google Brain team on the Kaggle platform. Here we show differentrepresentation of sounds (Wave frames, Spectrograms, Mel-Spectrograms, MFCCs)and apply several 1D and 2D convolutional neural networks in order to get thebest performance. Our experiments show that we found appropriate soundrepresentation and corresponding convolutional neural networks. As a result weachieved good classification accuracy that allowed us to finish the challengeon 8-th place among 1315 teams.

Deep Learning Approaches for Understanding Simple Speech Commands