A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN

Abstract

In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event from audio information, and the use of audio surveillance systems can greatly improve the robustness and reliability of event detection. In this paper, a novel system for the detection of polyphonic urban noise is proposed for on-campus audio surveillance. The system aggregates different acoustic features to improve the classification accuracy of urban noise. A combination model composed of a capsule neural network (CapsNet) and recurrent neural network (RNN) is employed as the classifier. CapsNet overcomes some limitations of convolutional neural networks (CNNs), such as the loss of position information after max-pooling, and the RNN mainly models the temporal dependency of context information. The combination of these networks further improves the accuracy and robustness of polyphonic sound events detection. Moreover, a monitoring platform is designed to visualize noise maps and acoustic event information. The deployment architecture of the system is used in real environments, and experiments were also conducted on two public datasets. The results demonstrate that the proposed method is superior to existing state-of-art methods for the polyphonic sound event detection task.

Application of Combined Filtering in Thunder Recognition

“…Each batch of data was trained 80 times. Finally, the frequency domain sub-band variance algorithm [29] is used to obtain the endpoints of thunder, in which the criteria are determined using the leading noise segment before each thunder signal.…”

“…Since various environmental interference signals may be recorded together with thunder, a reliable filtering method may improve the accuracy of thunder recognition. In this technical note, we applied four filtering techniques to thunder recognition, including low-pass filtering, a least mean square (LMS) adaptive algorithm, spectral subtraction, and Wiener filtering [28][29][30]. In addition, the impact of combinations of different filters is analyzed.…”

Adaptive Noise Reduction Algorithm Based on SPP and NMF for Environmental Sound Event Recognition under Low-SNR Conditions

“…Environmental Sound Recognition Systems. To verify the effectiveness of the proposed algorithm in improving the performance of the recognition systems under real scenarios, the recognition system designed in the previous work [40,41] was used in this paper for real-time environmental sound data acquisition and recognition processing. To ensure the authenticity of the experiment, a car horn was collected from the Guilin University of Electronic and Technology.…”

You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection

“…Vesperini et al [51] adopted a Capsule Neural Network (CapsNet) and binaural short-time Fourier transform (STFT) for feature extraction and obtained an error rate 0.58. Luo et al [52] presented a Capsule Neural Network Recurrent Neural Network (CapsNet-RNN) that obtained an error rate of 0.57. However, these optimisations were beyond the scope of this study.…”