A Survey on the Feasibility of Sound Classification on Wireless Sensor Nodes

Abstract

Wireless sensor networks are suitable to gain context awareness for indoor environments. As sound waves form a rich source of context information, equipping the nodes with microphones can be of great benefit. The algorithms to extract features from sound waves are often highly computationally intensive. This can be problematic as wireless nodes are usually restricted in resources. In order to be able to make a proper decision about which features to use, we survey how sound is used in the literature for global sound classification, age and gender classification, emotion recognition, person verification and identification and indoor and outdoor environmental sound classification. The results of the surveyed algorithms are compared with respect to accuracy and computational load. The accuracies are taken from the surveyed papers; the computational loads are determined by benchmarking the algorithms on an actual sensor node. We conclude that for indoor context awareness, the low-cost algorithms for feature extraction perform equally well as the more computationally-intensive variants. As the feature extraction still requires a large amount of processing time, we present four possible strategies to deal with this problem.

Inferring Human Activity Recognition with Ambient Sound on Wireless Sensor Nodes

“…In previous work [1], we calculated the Relative Execution Time (RET) for frequently-used features for sound recognition (see Table 2). The RET is a measure of the effort necessary for a certain type of calculation.…”

“…In prior experiments, we used the Jennic JN5148 platform to determine the RET for feature extraction algorithms [1]. …”

“…In our formulas, we use $s(n)$ to indicate the values of the original sound signal, N for the number of sound samples, $p(f)$ to indicate the power spectrum value for a certain frequency, and F for the number of Fourier values. We previously described these calculations in [1]. …”

“…In a previous article [1], we investigated feature extraction and classification by looking at the work of other researchers that have worked in the area of sound classification. We related the achieved accuracies of their work to the Relative Execution Time (RET) of the feature extraction algorithms.…”

Human Action Recognition with RGB-D Sensors

“…Multiple audio sensors in the same apartment could constitute a wireless sensor network (WSN), addressing the challenges of limited amount of memory and processing power of the nodes. However, it has been proven that low complexity features extraction algorithms can be adopted with good performance considering the indoor scenario [17]. Vuegen et al [18] proposed a WSN constituted by seven nodes placed in different rooms: living room/kitchen, bedroom, bathroom and toilet, covering the entire apartment.…”