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