# Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring
## 1 Introduction
This type of sound is typically broadband, but tonal components can occur due to the smoothing of the edges, or from the action of the airflow on cracks and holes [14]. In evaluating the noise emitted in a residential area, it is essential to identify all the sound sources that contribute to the annoyance caused to residents. Often this procedure is made difficult by the simultaneous contribution of different sources of a different nature which overlap. The acoustic characterization of these sources makes it possible, in some cases, to isolate these contributions, which can thus be removed or added. In this way, it is possible to evaluate the contribution of every single source to the perceived disturbance. When the frequency contributions of different sources overlap, this isolation is no longer possible, at least with the use of classic techniques. In these cases, it becomes essential in the noise disturbance assessment procedure to identify the presence of a specific source within the ambient noise [15]. In recent times, the use of machine learning-based algorithms for the identification of patterns in complex environments has become widespread [16; 17; 18; 19]. At first, these technologies were widely used in the field of computer vision [20; 21], but soon proved to be useful for identifying sources in complex sound environments [22; 23]. The idea behind the concept of machine learning is to provide computers with the ability to learn and replicate certain operations without having been explicitly programmed. These operations are generally of a predictive or decision-making type and are based on the data available. The machine, therefore, will be able to learn to perform certain tasks by improving, through experience, its skills, responses, and functions. At the basis of machine learning, there are a series of different algorithms that, starting from primitive notions, will process a result. A distinction between the various algorithms is given by the type of learning performed: depending on the training mode, supervised and unsupervised learning can be distinguished. In the first case, the model is trained by providing labeled data as an input, so in addition to the value of the chosen feature, a label with the class to which the data belongs will also be available [24]. On the contrary, to algorithms that exploit unsupervised learning, datasets are given without labels, and it is therefore the program that must find the classes in which to divide the data through features [25]. Then, depending on the different types of task they perform, the algorithms take various names. Classifiers are algorithms that, having input data and the classes to which they belong, can develop a way to understand which class a datum belongs to if they have never seen it before. Another type of algorithm is clustering algorithms, which can divide data taken from inputs without knowing the classes to which they belong. There are also regression algorithms that try to predict the value of data. Using other inputs of the same phenomena, they try to understand their trends by predicting the value of an unknown datum via interpolation [26]. In this study, the measurements of the noise emitted by a wind turbine carried out inside a house were used to develop a classifier capable of discriminating between the operating conditions of the turbine. To do this, a model based on artificial neural networks has been built which uses the sound pressure levels at different frequencies as inputs and returns the operating conditions of the turbine. The article is developed as follows: Section 2 describes in detail the methodologies used in the study: first, the instruments used to carry out the acoustic measurements and the noise detection techniques are described, along with the algorithms used to elaborate the numerical simulation model based on the support vector machine (SVM).