## 3 Results
### Offline Case Study Evaluation  
In this section, we describe the results provided by the deep learning models based on CNN and LM and MFCC representation for ambient sound recognition with the data collected and a public dataset in an offline context using 10-fold cross-validation. * Ad hoc ambient audio dataset. In this case, the dataset includes audio samples which have been collected in a single home and were labelled with an explicit segmentation of 3 s for events occurred in controlled conditions using the approach described in Section 2.1. All classes described in Table 2 are included in the dataset. * Audioset dataset (Repository: [[https://research.google.com/audioset/](https://research.google.com/audioset/)]([https://research.google.com/audioset/](https://research.google.com/audioset/)) (last access 15 July 2021)). This public dataset provides videos from YouTube and labelling in the segment where a given sound occurs. From the categories of the dataset, we selected 12 events related to our classes which are included in the dataset: \"Toilet flush\", \"Conversation\", \"Dishes, pots, and pans\", \"Alarm clock\", \"Water\", \"Water tap\", \"Printer\", \"Microwave oven\", \"Doorbell\", \"Door\", \"Telephone ringing\" and \"Silence\". The sounds collected from Audioset correspond to a balanced dataset with 60 files for each class which includes an explicit segmentation of the sound events. For each dataset, we present a comparison of the confusion matrices for each fold in the cross-validation that was computed. First, in Figure 4 we present the performance of the DL models in ambient sound recognition of daily events for the ad hoc dataset. Second, in Figure 5, we present the performance of the DL models in the Audioset dataset. In \begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Description** \\ \hline Vaccum cleaner & Audio sample of vacuuming \\ Tank & Audio sample of flushing toilet \\ Cutlery + pans & Audio sample of cutlery and pans \\ Alarm clock & Audio sample of alarm clock sound \\ Shower & Audio sample of shower \\ Extractor & Audio sample of an extractor fan \\ Kitchen tap & Audio sample of a kitchen tap \\ Bathroom tap & Audio sample of a bathroom tap \\ Printer & Audio sample of a printer operating \\ Microwave & Audio sample of a microwave operating \\ Blind & Audio sample of a window blind being moved \\ Door & Audio sample of a door being opened or closed \\ Phone & Audio sample of a phone ringing \\ Doorbell & Audio sample of a doorbell ringing \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sound events and descriptions developed in the context of daily activities. Table 3, we describe the metrics of f1 score, precision, and recall for both DL models, and the evaluated datasets. As can be observed, the performance of the ad hoc ambient audio dataset has excellent results for both CNN models in controlled conditions, CNN + MFCC showing the best results. However, the performance in sound recognition of daily events with the Audioset dataset is highly deficient. This is due to the fact that the audio samples from YouTube videos include noise overlapping with other sounds and audio generation from heterogeneous sources. For interested readers, the Audioset samples are available in the repository of this work. As we can observe, the collection of an ad hoc ambient audio dataset is strongly recommended given the weak sampling from heterogeneous sources. From the ad hoc ambient audio dataset, we have collected the number of trainable parameters, learning time, millions of instructions (up to 40 epochs) and evaluation time in a Raspberry Pi 3B whose core frequency is 400 MHz, presented in Table 4.  
Based on these results, in the next section, we describe the evaluation in real-time conditions using the best configuration with the ad hoc ambient audio dataset and the model based on CNN + MFCC which also requires fewer computational resources for audio recognition learning and evaluation. Figure 4: Confusion matrices in ad hoc ambient audio dataset.