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

arXiv: https://arxiv.org/abs/1809.00238 · HTML: https://mlsanity.com/paper/1809.00238/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM](https://mlsanity.com/paper/2211.09814/) (2022)

## Abstract (arXiv)

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.