## 1 Introduction
The aim of the DCASE community is the support development of computational scene and event analysis methods by providing public datasets [12; 13], and giving researches the opportunity to continuously compare different approaches on the same datasets, using consistent performance measures. The challenges \"Urban Sound Tagging with Spatiotemporal context\", organized in 2020, and \"Acoustic Scene Classification\", organized in 2021 [14], had important contributions in the sound-classification area. For these classification tasks, the majority of researchers use deep neural network (DNN) architectures that perform rather well as they have the ability to extract discriminative feature representations. The most popular architectures are based in Convolutional Neural Networks (CNN) [6; 15; 16]. Some other researches, motivated by the fact that the CNNs do not learn long-term dependencies, propose solutions based on CNNs combined with Recurrent Neural Networks (RNN) [17; 18; 19]. However, the RNNs suffer from the vanishing gradient problem. To overcome this problem the ResNets [20; 21; 22] were introduced, as they use residual blocks that enable training a large number of layers. More recently, transformer architectures purely based on attention mechanisms [23; 24; 25] and hybrid architectures combining transformers with RNNs [26] and ResNets [27], have been proposed. Most of the systems apply data augmentation and transfer learning techniques. Some other systems propose the fusion of different classifiers [28; 29; 30; 31] and features [32; 33]. Good outcomes depend on the network architecture selected, which is influenced by the problem to be solved, the quantity and quality of the data available and other factors. Despite the widespread use of DNNs, researchers are still facing some challenges as the lack of annotated data for certain tasks, especially for real-world applications. The ESC dataset [34], UrbanSound8k [35], AudioSet [36], and the more recently released SONYC-UST [13] and SONYC-USTv2 [37] datasets are the most popular and openly accessible datasets that are frequently utilized in the development and evaluation of urban sound classification systems. The ESC dataset consists of three parts. Two of them, ESC-10 and ESC-50, comprise a labelled set of 10 and 50 classes of different environmental sounds while the third part, ESC-US, is a set of non-labelled data. All clips were extracted from the public field recordings available in the Freesound project [38]. UrbanSound8k contains labelled recordings for 10 classes of urban sounds, also extracted from the Freesound project. In turn, AudioSet corpus consists of labelled sounds from different sources (domestic, environmental, nature, music,...) drawn from YouTube. The majority of the aforementioned datasets have the drawback of not having been recorded for monitoring urban noise. SONYC project [9], addresses this problem, deploying a number of noise monitoring sensors for collecting data. The aim of collecting the data was to develop a classification system for a specific task, namely to confirm that the complaints made by residents about violations of the noise code of New York City were true. SONYC-UST and SONYC-USTv2 are multi-label datasets for urban sound tagging that comprise a fine labelled set of 23 classes that are grouped into 8 coarse classes with more general descriptors. The main difference between them is that the latter includes spatio-temporal context information about the recording event. More recently, the so called SINGA-PURA database has been published [39] following the structure of SONYC-UST V2 taxonomy and expanding some classes in more detail. In any case, more datasets with samples from real application scenarios are still needed in this area. In this line, we present a novel audio database, the so called NoisenseDB, with real urban sound events intended to be used in sound-classification tasks. The recording of this database was performed by deploying a continuous monitoring system developed by the company NOISMART in real urban locations.