Robust sound event recognition using convolutional neural networks

Traditional sound event recognition methods based on informative front end features such as MFCC, with back end sequencing methods such as HMM, tend to perform poorly in the presence of interfering acoustic noise. Since noise corruption may be unavoidable in practical situations, it is important to develop more robust features and classifiers. Recent advances in this field use powerful machine learning techniques with high dimensional input features such as spectrograms or auditory image. These improve robustness largely thanks to the discriminative capabilities of the back end classifiers. We extend this further by proposing novel features derived from spectrogram energy triggering, allied with the powerful classification capabilities of a convolutional neural network (CNN). The proposed method demonstrates excellent performance under noise-corrupted conditions when compared against state-of-the-art approaches on standard evaluation tasks. To the author's knowledge this in the first application of CNN in this field.