Very short time environmental sound classification based on spectrogram pattern matching
Environmental sounds are unstructured and similar to noise. However, the recognition of environmental sounds can benefit crime investigations, warning systems for elderly persons, and security systems. A few past research projects were developed for classifying the environmental sounds. In this paper, we proposed an environmental sound classification algorithm using spectrogram pattern matching along with neural network and k-nearest neighbor (k-NN) classifiers. Unlike other techniques, our approach is based on the observation that local features are more important than global features. In addition, our technique can avoid the problem of filtering less informative and irrelevant frequencies in the classification step. Twenty types of sound from BBC and Sound Ideas databases, with each sound sample longer than 10min, were tested with our algorithm. The spectrogram feature was compared with mel frequency delta cepstral coefficient (MFCC), linear prediction coefficient (LPC), and matching pursuit (MP) features. Two relevant factors concerning the accuracy of classification, window size and sampling rate, were also investigated to find the suitable value of each factor. We also investigated all combinations of these features. Using the k-NN classifier, the maximum accuracy of 94.98% occurred when the spectrogram, LPC, and MP features were combined. The experiments showed that the spectrogram feature with a feed-forward neural network can effectively classify 1-s audio clips with an accuracy of 85.66%. Furthermore, a longer duration can significantly increase the accuracy of classifying each sound clip to a certain limit without affecting cost. Using an audio clip duration of 6s, the spectrogram feature with a feed-forward neural network provided the best classification accuracy, at least 90.57%.
spectrogram spectrogram pattern matching environmental sound recognition k-nearest neighbourneighbor (k-nn)