Polyphonic Sound Event Detection Using Transposed Convolutional Recurrent Neural Network

Chatterjee, Chandra Churh; Mulimani, Manjunath; Koolagudi, Shashidhar G.

doi:10.1109/icassp40776.2020.9054628

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…The RNN is a type of artificial neural network that is especially suitable for processing sequential information such as natural languages or time series data such as videos [ 56 , 57 ]. Applications of RNNs include handwriting recognition [ 58 ], speech recognition [ 59 ], gesture recognition [ 60 ], image captioning [ 61 ], natural language processing [ 62 ] and understanding [ 63 ], sound event prediction [ 64 ], tracking and monitoring [ 65 , 66 , 67 , 68 , 69 ], etc.…”

Section: Deep Learning Models and Methodsmentioning

confidence: 99%

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu

Wang

Yin

et al. 2022

Sensors

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Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

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Section: Deep Learning Models and Methodsmentioning

confidence: 99%

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu

Wang

Yin

et al. 2022

Sensors

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“…Ding et al [30] proposed a Convolutional Recurrent Neural Network (CRNN) able to identify shortterm dependencies of audio patterns by applying a multiscale detection method. With a similar approach, Chatterjee et al [31] introduced a Transposed Convolutional recurrent Neural Network (TCRNN) that incorporates Mel Instantaneous Frequency spectrogram (mel-IFgram) features. However, recurrent layers in neural networks are subject to the vanishing gradient problem, leading to lack of generalization capabilities.…”

Section: Related Work a Deep Learning Modelsmentioning

confidence: 99%

Sound Event Detection for Human Safety and Security in Noisy Environments

et al. 2022

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The objective of a sound event detector is to recognize anomalies in an audio clip and return their onset and offset. However, detecting sound events in noisy environments is a challenging task. This is due to the fact that in a real audio signal several sound sources co-exist and that the characteristics of polyphonic audio are different from isolated recordings. It is also necessary to consider the presence of noise (e.g. thermal and environmental). In this contribution, we present a sound anomaly detection system based on a fully convolutional network which exploits image spatial filtering and an Atrous Spatial Pyramid Pooling module. To cope with the lack of datasets specifically designed for sound event detection, a dataset for the specific application of noisy bus environments has been designed. The dataset has been obtained by mixing background audio files, recorded in a real environment, with anomalous events extracted from monophonic collections of labelled audios. The performances of the proposed system have been evaluated through segment-based metrics such as error rate, recall, and F1-Score. Moreover, robustness and precision have been evaluated through four different tests. The analysis of the results shows that the proposed sound event detector outperforms both state-of-the-art methods and general purpose deep learning-solutions.

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“…Many works [24], [25], [26] have proven that temporal dependency is significant in detecting sound events. In this research, an RNN is used to add the unique position relationship of sound events by learning the temporal context information.…”

Section: ) Recurrent Layersmentioning

confidence: 99%

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

et al. 2021

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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.

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Polyphonic Sound Event Detection Using Transposed Convolutional Recurrent Neural Network

Cited by 6 publications

References 10 publications

Deep Learning in Diverse Intelligent Sensor Based Systems

Deep Learning in Diverse Intelligent Sensor Based Systems

Sound Event Detection for Human Safety and Security in Noisy Environments

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

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