## 1 Introduction
This dataset was used to perform methods for both identification [9] and categorization [10] of acoustic scenes and events. Recently, a deep learning structure was created using this dataset [11] to retrieve urban sound events such as car horns and human speech from multi-label audio recordings. In a European project called DYNAMAP [12], multiple machine learning techniques were evaluated for detecting [13; 14; 15; 16] abnormal noise sources such as birds, bikes, vehicles with heavy loads passing over rough surfaces, horn vehicle noise, music in a car or in the street, ambulance sirens, airplanes, thunder storms, etc., in order to eliminate events unrelated to road traffic noise and create a noise map. In addition to the previously mentioned methods, other techniques utilizing supervised machine learning have been utilized for classifying sound sources. [PERSON] et al. [17] introduced a pattern classification algorithm that used Mel-frequency cepstral coefficients as features to determine the primary noise source in the acoustic environment. In this research, two types of supervised classifiers, namely, artificial neural networks with two hidden layers of 10, 30, 50, or 100 neurons and a Gaussian mixture model, were compared. [PERSON] et al. [18] introduced an aggregation scheme combining local features and short-term sound recording features with long-term descriptive statistics to create a deep convolutional neural network for classifying urban sound events. Regarding machine learning techniques for sound pressure level and acoustic pattern prediction, a number of studies have been published within the last few years. [PERSON] et al. [19; 20] proposed an ANN architecture with only one hidden layer to predict annoyance levels of traffic noise. The architecture complexity of the trained ANNs (see Section 2.4 for details about this definition) were _Net_3\(1\), with six variables in the input layer for the first study and _Net_1\(1\), _Net_2\(1\), _Net_3\(1\), _Net_4\(1\), _Net_5_1 with five variables in the input layer for the second study. By utilizing short-term data and concentrating on traffic noise, unsupervised machine learning techniques such as dimensionality reduction and clustering were employed to optimize the location and quantity of monitoringsites [21]. A separate publication [22] presented a methodology for more efficiently estimating day-period and night-period sound pressure levels on urban roads in Milan, Italy in comparison to the legislative road classification by using equivalent sound pressure levels of a 1-h period from a 24 h measurement campaign. Subsequently, in order to link each street in the area of examination to one of the two noise profiles found through clustering, several non-acoustic parameters were examined [22]. In another recent study, the intermittency ratio indicator was paired with the equivalent sound pressure level of a 1-h time frame in order to improve the categorization of different types of streets within the two identified clusters [23]. Regarding the identification of the long-term environmental acoustic pattern of a city, [PERSON] et al. [24] investigated the necessary stabilization time, short-term variability, and impulsiveness of the sound pressure level to accurately characterize the temporal composition of urban soundscapes. The authors used data from sound level meters to analyze sound pressure levels in urban environments, and found that a stabilization time of at least 30 min was required to obtain reliable measurements of sound pressure level. The same study suggested that measurements should be taken over a longer period of time to achieve a more accurate characterization of urban soundscapes, and that the short-term variability and impulsiveness of sound pressure levels should be considered as well. In a later study, [PERSON] et al. [25] analyzed data collected from sound level meters in various urban environments and concluded that hourly averages of sound levels may not be representative of the true levels of noise exposure.