## 5 Classification Experiments
### Semi-Supervised Strategy for Automatic Labelling  
As we have mentioned before, one of the issues of obtaining datasets to train urban-sound-event classifying systems is the initial labelling of the sound events that are registered by the monitoring devices. In this work, for the supervised part (SD) of the database, we used the 72 dB(A) threshold to obtain a manageable set of clips that could be labelled manually. However, we wanted to tackle the problem of automating the labelling for larger datasets. With this objective, a new set of events, with SPLs between 71 and 72 dB(A)s, was extracted from the recordings and manually labelled. This dataset was used to experiment with unsupervised training and, thus, was called the unsupervised dataset (UD). We presented a novel semi-supervised strategy to train a classifier for automatic labelling. Such a strategy can be applied to create the final classifier directly, but also as a way of obtaining an initial labelling that can be refined by human labellers afterwards. In order to evaluate the proposed strategy, we carried out an experiment using the convolutional architectures. The proposed semi-supervised training method consists of several training and labelling iterations using labelled data (SD part) and unlabelled one (UD part). In the process the unlabelled data is automatically labelled and the accuracy of this labelling is refined iteratively. We have applied cross-validation for the evaluation of the method and thus the semi-supervised training iterative process that we describe bellow have been repeated 5 times using different folds of the SD part as train and validation data. The process is depicted in Figure 7.  
For a particular cross-validation iteration (k), we reserve one of the folds of the SD part of the dataset for validation SDvk and the rest 4 folders are the labelled part for training (SDtk). On the first iteration of the semi-supervised labelling process (iter0 or i = 0), we train the system using the labelled part, SDtk. UDk(\({}^{-1}\)) is not used in this initial training because it has no labels, i.e., UDk(\({}^{-1}\)) is empty. Once the training is finished, we obtain the labels for two different groups of data. The first group is the unlabelled part (getting the predicted labels UDk(\({}^{0}\))), and the second one is the reserved validation fold of SDvk data, getting the corresponding SDvk(\({}^{0}\)) labels. In the second iteration (i = 1), the model is trained again but in this case the UD dataset with the new labels automatically predicted in the previous iteration UDk(\({}^{0}\)), is added to the initial training set (the SDtk part). The resulting model, Modelk(\({}^{1}\)) is used to relabel the UD getting UDk(\({}^{1}\)) and the validation SDvk, getting a new set of labels, SDvk(\({}^{1}\)). We repeat the process for four more iterations and analyse the recall of the labelling obtained in each iteration for the UD part, for the SDvk part and the combination of both (SDvk&UD) in order to see the overall effect in the  
Figure 6: Overall confusion matrix for the AST at the fragment level. Data in %. classification performance. The idea is to check if the system is able to improve using its own predictions of the unsupervised data in a self-convergence iteration sequence. As we have mentioned before, in the following experiments this process has been performed 5 times using 4 different folds to compose the training dataset \(\text{SDt}_{\text{k}}\) and the remaining fold as validation dataset \(\text{SDv}_{\text{k}}\). The UD part is used entirely in every cross-validation iteration, without dividing it in folds. We have decided to do so because its labelling ground truth information is never used in the training process, and because the amount of samples of this part was not very big.