## 1 Introduction
As described above, ambient binary sensors have been proposed to describe daily activities in indoor spaces [14] with the goal of deploying immersive sensors, providing encouraging results with accurately labelled datasets [15] under data-driven approaches [10]. Nowadays, the burgeoning growth of devices is promoting multimodal sensors which typically integrate video, audio, and wearable sensors [16], and other IoT devices with increasing high-capacity computing. The new trends are converging toward synthetic sensors [17], which are deployed to sense everything in a given room, enabling the use of general-purpose sensing technologies in order to monitor activities by means of sensor fusion. In this context, audio processing by smart microphones for the labelling of audible events is opening up a promising research field within AR [8]. On the architecture of components for learning and communication of devices, the paradigms of edge computing [18] or fog computing [19] have located the data and services within the devices where sensors are integrated, _providing virtualised resources and engaged location-based services at the edge of the mobile networks_ under a new perspective of the Internet of Things (IoT) to develop collaborative smart objects which _interact with each other and cooperate with their neighbours to reach common goals_[20]. In the machine learning models for AR, describing sensor information under data-driven approaches has depended on the type of sensors, whether inertial [5] or binary sensors [15], where integrated methodologies to exploit spatial-temporal features have been proposed [21]. Additionally, deep learning (DL) has also been shown as a suitable approach in AR to discover and extract features from sensors [22]. DL is related to multimodal sensor recognition, such as vision and audio, where obtaining hierarchical features to reduce complexity is key. Regarding vision sensors, the use of thermal vision is proposed to guarantee privacy while preventing dangers such as falling by means of convolutional neural networks (CNNs) [23]. In the field of audio recognition, the combination of CNNs [24] with the use of spectrogram for sound representation [25] has been proven to generate encouraging results in sound recognition, which can be used for environmental sound classification [26; 27; 28] and music signal analysis [29; 30]. Specifically, both the use of log-Mel spectrogram (LM) and Mel-frequency cepstral coefficient (MFCC) has been proposed for robust representation in sound classification [31]. In the given field of environmental sound recognition in indoor spaces, we highlight several approaches. In [32], the recognition of events, such as a bouncing ball or cricket, was carried out by means of spectral representation of sound with frame-level features, which was learned using Markov models. In [33], two classes of sounds (i.e., tapping and washing hands) were recognised using spectral and histogram of sounds by SVM in naturalistic conditions within a geriatric residence. In part of the study by [8], 3D spatial directional microphones allowed high-quality multidirectional audio to be captured to detect events and the location of sounds in an environment. For this purpose, Mel-frequency cepstral coefficients are computed as spatial features which are related to events using Gaussian and hidden Markov models. In [34], 30 events were collected to recognise the 7 rooms or spaces where the inhabitant carried out activities (bathroom, bedroom, house entrance, kitchen, office, outdoor, and workshop). In this work, log-Mel spectrograms were also evaluated for sound event classification, together with a DL model (VGG-16) pretrained with YouTube audios, with encouraging results but where accuracy was demonstrated to differ notably between controlled conditions and real-life contexts. Moreover, fuzzy logic has been demonstrated to provide suitable sensor representation from the first AR methods [35] to recent works [21].