Nowadays, urban noise emerges as a distinct threat to people’s physiological and psychological health. Previous works mainly focus on the measurement and mapping of the noise by using Wireless Acoustic Sensor Networks (WASNs) and further propose some methods that can effectively reduce the noise pollution in urban environments. In addition, the research on the combination of environmental noise measurement and acoustic events recognition are rapidly progressing. In a real-life application, there still exists the challenges on the hardware design with enough computational capacity, the reduction of data amount with a reasonable method, the acoustic recognition with CNNs, and the deployment for the long-term outdoor monitoring. In this paper, we develop a novel system that utilizes the WASNs to monitor the urban noise and recognize acoustic events with a high performance. Specifically, the proposed system mainly includes the following three stages: (1) We used multiple sensor nodes that are equipped with various hardware devices and performed with assorted signal processing methods to capture noise levels and audio data; (2) the Convolutional Neural Networks (CNNs) take such captured data as inputs and classify them into different labels such as car horn, shout, crash, explosion; (3) we design a monitoring platform to visualize noise maps, acoustic event information, and noise statistics. Most importantly, we consider how to design effective sensor nodes in terms of cost, data transmission, and outdoor deployment. Experimental results demonstrate that the proposed system can measure the urban noise and recognize acoustic events with a high performance in real-life scenarios.

Deep learning (DL)-based side-channel analysis (SCA) has posed a severe challenge to the security and privacy of embedded devices. During its execution, an attacker exploits physical SCA leakages collected from profiling devices to create a DL model for recovering secret information from victim devices. Despite this success, recent works have demonstrated that certain countermeasures, such as random delay interrupts or clock jitters, would make these attacks more complex and less practical in real-world scenarios. To address this challenge, we present a novel denoising scheme that exploits the U-Net model to pre-process SCA traces for “noises” (i.e., countermeasures) removal. Specifically, we first pre-train the U-Net model on the paired noisy-clean profiling traces to obtain suitable parameters. This model is then fine-tuned on the noisy-only traces collected from the attacking device. The well-trained model will be finally deployed on the attacking device to remove the noises (i.e., countermeasures) from the measured power traces. In particular, a new inductive transfer learning method is also utilized in our scheme to transfer knowledge learned from the source domain (i.e., profiling device) to the target domain (i.e., attacking device) to improve the model’s generalization ability. During our experimental evaluations, we conduct a detailed analysis of various countermeasures separately or combined and show that the proposed denoising model outperforms current state-of-the-art work by a large margin, e.g., a reduction of at least 30% in computation costs and 5× in guessing entropy.

This paper considers the task of indoor subarea localization by acoustic sensing. In many practical scenarios, such as ordinary office rooms, subarea localization based on a single acoustic property without subsidiaries is always tricky due to similar space physical structures. To combat the indoor subarea localization problem without any dedicated devices, we propose a framework allowing the ordinary smartphone to quickly and easily determine its indoor subarea location. The proposed structure is to first construct the multidimensional fingerprint of each environmental background audio in a room using the auditory scene analysis method; then extract the room-level fingerprint by the acoustic background spectrum method, and cluster the subarea-level fingerprints using the Pearson correlation coefficient technology; subsequently, together with the offline stage long short-term memory recurrent neuron network and online stage least variance algorithm, the effective indoor subarea localization was achieved; finally, the localization results are presented not only in text form but also in a privacy-protected color sound pattern which is built by chroma DCT-reduced log pitch. Our experiments with real-world audios, which include 96-h environment background audios of 14 different rooms, presents that the room-level localization accuracy was higher than 90% and the subarea-level accuracy could be up to 97.64%. These results strongly proved that the proposed framework could perform well even in small complicated rooms.

The ubiquity of sensor-rich smartphones provides opportunities for a low-cost method to track indoor pedestrians. In this situation, pedestrian dead reckoning (PDR) is a widely used technology; however, its cumulative error seriously affects its accuracy. This paper presents a method of combining infrastructure-free indoor acoustic self-positioning with PDR self-positioning, which verifies the rationality of PDR results through the acoustic constraint between a sound source and its image sources. We further determine the first-order echo delay measurements, thus obtaining the mobile user position. We verify that the proposed method can achieve a continuous self-positioning median error of 0.19 m, and the error probability below 0.12 m is 54.46%, which indicates its ability to eliminate PDR error, as well as its adaptability to environmental disturbances.

Sidelobe suppression is a major challenge in wideband beamforming for acoustic research, especially in high noise and reverberation environments. In this paper, we propose a multi-objective NSGA-II wideband beamforming method based on a spherical harmonic domain for spherical microphone arrays topology. The method takes white noise gain, directional index and maximum sidelobe level as the optimization objectives of broadband beamforming, adopts the NSGA-II optimization strategy with constraints to estimate the Pareto optimal solution, and provides three-dimensional broadband beamforming capability. Our method provides superior sidelobe suppression across different spherical harmonic orders compared to commonly used multi-constrained single-objective optimal beamforming methods. We also validate the effectiveness of our proposed method in a conference room setting. The proposed method achieves a white noise gain of 8.28 dB and a maximum sidelobe level of −23.42 dB at low frequency, while at high frequency it yields comparable directivity index results to both DolphChebyshev and SOCP methods, but outperforms them in terms of white noise gain and maximum sidelobe level, measuring 16.14 dB and −25.18 dB, respectively.