text: 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.
  @cite_16: The noise pollution is still a troubling problem for us, especial in hospital, library, baby-care center, etc. Although many decibel-meters are applied to those environment, which indeed must be kept quit, but the decibel-meters can only detect the noise pollution limited to nearest area around the device. We will face the incorrect measuring data from the decibel-meters in bigger space like library. According such problems mentioned above, maybe we use more decibel-meters around different areas to a better solution.
  @cite_10: Noise pollution caused by vehicular traffic is a common problem in urban environments that has been shown to affect people's health and children's cognition. In the last decade, several studies have been conducted to assess this noise, by measuring the equivalent noise pressure level (called L eq ) to acquire an accurate sound map using wireless networks with acoustic sensors. However, even with similar values of L eq , people can feel the noise differently according to its frequency characteristics. Thus, indexes, which can express people's feelings by subjective measures, are required. In this paper, we analyze the suitability of using the psychoacoustic metrics given by the Zwicker's model, instead of just only considering L eq . The goal is to evaluate the hardware limitations of a low-cost wireless acoustic sensor network that is used to measure the annoyance, using two types of commercial and off-the-shelf sensor nodes, Tmote-Invent nodes and Raspberry Pi platforms. Moreover, to calculate the parameters using these platforms, different simplifications to the Zwicker's model based on the specific features of road traffic noise are proposed. To validate the different alternatives, the aforementioned nodes are tested in a traffic congested area of Valencia City in a vertical and horizontal network deployment. Based on the results, it is observed that the Raspberry Pi platforms are a feasible low-cost alternative to increase the spatial-temporal resolution, whereas Tmote-Invent nodes do not confirm their suitability due to their limited memory and calibration issues.
  @cite_2: 
summary: @cite_2 provide an overview of a platform for distributed urban noise measurement, which is part of an ongoing German research project called . A wireless distributed network of audio sensors based on quad-core ARM BCM2837 SoC was employed to receive urban noise signals, pre-process the obtained audio data and send it to a central unit for data storage and performing higher-level audio processing. A final stage of web application was used for visualization and administration of both processed and unprocessed audio data. The authors in @cite_16 used Ameba RTL 8195AM and Ameba 8170AF as IoT platforms to implement a distributed sensing system for visualization of the noise pollution. In @cite_10 , two hardware alternatives, Raspberry Pi platform and Tmote-Invent nodes, were evaluated in terms of their cost and feasibility for analyzing urban noise and measuring the psycho-acoustic metrics according to the Zwicker's annoyance model. In contrast to related work, our approach is not concerned with measuring the noise level in dB using IoT, but with determining the type of noise (for instance, a jackhammer or gun shot).