## 4 Conclusions and Ongoing Work  
In this work, we evaluated the capabilities of audio recognition models based on spectral information and deep learning to identify ambient events related to the daily activities of inhabitants in a home. To this end, an edge-fog computing approach with smart boards was presented, which enabled the evaluation and recognition of audio samples within the devices while preserving the privacy of the users. Fuzzy processing of audio event streams was included in the IoT boards to filter the raw prediction of audio events by means of temporal restrictions defined by protoforms. The fuzzy processing of audio recognition proved crucial in real-time scenarios to avoid false positives and provide a coherent recognition of daily events detected from protoforms which are directly defined in linguistic terms.  
In ongoing research, we aim to integrate a fusion of heterogeneous sensors, such as wearable and binary sensors, to increase the sensing capabilities of audio recognition with other daily activity events. In addition, fuzzy rules could enhance the knowledge-based definition of activities with steady processing from raw data, integrating the data collected from different sensors.  
Conceptualization, J.M.-Q. and C.P.; machine learning model, [PERSON] and [PERSON]; recording of audio samples J.M.V.C.; validation,formal analysis, investigation, supervision, all authors. All authors have read and agreed to the published version of the manuscript.  
This contribution has been supported by the Andalusian Health Service by means of the project PI-0387-2018. Funding for this research is provided by EU Horizon 2020 Pharaon Project 'Pilots for Healthy and Active Ageing', Grant agreement No. 857188. Moreover, this research received funding under the REMIND project Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020, under grant agreement No. 734355.  
Not applicable.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Raw** & \multicolumn{3}{c}{**Fuzzy**} \\ \cline{2-5}  & **FP** & **FN** & **FP** & **FN** \\ \hline Printer & 6 & 0 & 0 & 0 \\ vacuum cleaner & 2 & 0 & 0 & 1 \\ blind & 3 & 0 & 0 & 0 \\ door bell & 1 & 0 & 0 & 0 \\ Kitchen tap & 6 & 0 & 0 & 0 \\ microwave & 1 & 0 & 0 & 0 \\ shower & 2 & 0 & 1 & 0 \\ tap wc & 3 & 0 & 1 & 0 \\ door & 0 & 0 & 0 & 1 \\ Total & 24 & 0 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: False positives (FP) and negatives (FN) computed from the time interval detection of home events using raw processing and fuzzy temporal restrictions.  
Informed Consent Statement: Not applicable.  
**Data Availability Statement:** The dataset of audio samples collected in this work and the labels of the scenes are available in the following repository [[https://github.com/AuroraPR/Ambiental-Sound-Recognition](https://github.com/AuroraPR/Ambiental-Sound-Recognition)]([https://github.com/AuroraPR/Ambiental-Sound-Recognition](https://github.com/AuroraPR/Ambiental-Sound-Recognition)) (last access 15 July 2021), which includes the implementation of the proposed methods with Python and Keras.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Sample Availability:** The dataset of audio samples collected in this work and the labels of the scenes are available in the next 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) which include the implementation with Python and Keras of the proposed methods.