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We present a machine learning based method for noise classification using alow-power and inexpensive IoT unit. We use Mel-frequency cepstral coefficientsfor audio feature extraction and supervised classification algorithms (that is,support vector machine and k-nearest neighbors) for noise classification. Weevaluate our approach experimentally with a dataset of about 3000 sound samplesgrouped in eight sound classes (such as, car horn, jackhammer, or streetmusic). We explore the parameter space of support vector machine and k-nearestneighbors algorithms to estimate the optimal parameter values forclassification of sound samples in the dataset under study. We achieve a noiseclassification accuracy in the range 85% -- 100%. Training and testing of ourk-nearest neighbors (k = 1) implementation on Raspberry Pi Zero W is less thana second for a dataset with features of more than 3000 sound samples.

A Machine Learning Driven IoT Solution for Noise Classification in Smart Cities