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_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).
@cite_16 @cite_10 @cite_2
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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. Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary information to navigate safely in the environment. In this paper, we present a framework that spots the presence of acoustic events, such as horns and sirens, using a two-stage approach. We first model the urban soundscape and use anomaly detection to identify the presence of an anomalous sound, and later determine the nature of this sound. As the audio samples are affected by copious non-stationary and unstructured noise, which can degrade classification performance, we propose a noise-removal technique to obtain a clean representation of the data we can use for classification and waveform reconstruction. The method is based on the idea of analysing the spectrograms of the incoming signals as images and applying spectrogram segmentation to isolate and extract the alerting signals from the background noise. We evaluate our framework on four hours of urban sounds collected driving around urban Oxford on different kinds of road and in different traffic conditions. When compared to traditional feature representations, such as Mel-frequency cepstrum coefficients, our framework shows an improvement of up to 31 in the classification rate. This paper proposes a new method for bimodal information fusion in audio-visual speech recognition, where cross-modal association is considered in two levels. First, the acoustic and the visual data streams are combined at the feature level by using the canonical correlation analysis, which deals with the problems of audio-visual synchronization and utilizing the cross-modal correlation. Second, information streams are integrated at the decision level for adaptive fusion of the streams according to the noise condition of the given speech datum. Experimental results demonstrate that the proposed method is effective for producing noise-robust recognition performance without a priori knowledge about the noise conditions of the speech data.