Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Machine Learning Driven IoT Solution for Noise Classification in Smart
  Cities   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.

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The categories of this paper are: hep-ex
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The categories of this paper are: math
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin