Emergency Signal Classification for the Hearing Impaired using Multi-channel Convolutional Neural Network Architecture

Padhy, Swarup; Tiwari, Juhi; Rathore, Shivam Singh; Kumar, Neetesh

doi:10.1109/cict48419.2019.9066252

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…Studies have also been conducted to detect emergency situations using urban sounds. Padhy et al [21] proposed a multi-channel CNN to recognize emergency situations for hearing-impaired individuals. Tran and Tsai [29] proposed SirenNet that can identify emergency vehicles.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Urban Noise Analysis and Emergency Detection System using Lightweight End-to-End Convolutional Neural Network

Park,

Yoo,

Lee

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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In recent years, the application of deep learning to environmental sound classification (ESC) has received considerable attention owing to its powerful ability to recognize the context of urban sounds. In general, deep learning models with high accuracy require substantial computing and memory resources. Consequently, to apply complex deep learning models to ESC in the real world, model inference has been performed on cloud servers with powerful computing resources. However, heavy network traffic and security issues occur when inferences are performed on a cloud server. In addition, deploying a deep learning model trained on a single public ESC dataset may not be sufficient for classifying various classes of urban noise and emergency-related sounds. To address these problems, we propose an on-device, real-time urban sound monitoring system that can classify various urban sounds at low system construction costs. The proposed system consisted of an edge artificial intelligence (AI) node and a FIWARE-based server. To enable the real-time inference on a resource-constrained edge AI node, we developed a lightweight convolutional neural network (CNN) by adjusting the input and model configurations to achieve high accuracy with a low number of parameters. The model achieved 94.9% classification accuracy using only 331 K parameters on an integrated dataset that included 17 classes of urban noises and emergencies. Furthermore, a prototype of the proposed system was developed and evaluated to verify its feasibility. The prototype system was built at a cost of less than USD 50 and could perform the entire monitoring process every 3 s. We also visualized the sound monitoring data using Grafana on a FIWARE-based server.

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Section: Related Workmentioning

confidence: 99%

“…Moreover, since sound carries rich contextual information, ESC is widely used for intelligent urban sound monitoring in smart cities. For example, studies have been conducted to detect emergency situations, such as vehicle accidents, crimes, and sirens, from urban noise [3,21,29].…”

Section: Introductionmentioning

confidence: 99%

Urban Noise Analysis and Emergency Detection System using Lightweight End-to-End Convolutional Neural Network

Park,

Yoo,

Lee

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…The first step of our experiments aims to find the (C, K, p, n) combination that returns the best results in the classification of a target word (SWC), an environmental sound (US8K), and an ambulance siren (A3S-Synth). We train prototypical networks in the (C, K) ∈ {(2, 1), (2, 5), (5, 1), (5, 5), (10, 1), (10,5), (10, 10)} configurations with SWC and US8K datasets, while we employ (C, K) ∈ {(2, 1), (2, 5), (2, 10)} with A3S-Synth. At inference time, all models are evaluated by constructing positive and negative support embeddings in the same (p, n) combinations with p ∈ {1, 5} and n ∈ {1, 5, 10, 50}.…”

Section: Few-shot Models Analysismentioning

confidence: 99%

“…In Ref. [5], a multichannel convolutional neural network (CNN) has executed the task of non-emergency and emergency sound classification. The authors have retrieved the siren audio files from the massive collection of labeled data called AudioSet [6], further extended by using data augmentation techniques.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Emergency Siren Detection

Cantarini

Gabrielli

Squartini

2022

Sensors

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It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, but audio data collection and labeling are often challenging and require large amounts of effort. This paper proposes a workflow based on few-shot metric learning for emergency siren detection performed in steps: prototypical networks are trained on publicly available sources or synthetic data in multiple combinations, and at inference time, the best knowledge learned in associating a sound with its class representation is transferred to identify ambulance sirens, given only a few instances for the prototype computation. Performance is evaluated on siren recordings acquired by sensors inside and outside the cabin of an equipped car, investigating the contribution of filtering techniques for background noise reduction. The results show the effectiveness of the proposed approach, achieving AUPRC scores equal to 0.86 and 0.91 in unfiltered and filtered conditions, respectively, outperforming a convolutional baseline model with and without fine-tuning for domain adaptation. Extensive experiments conducted on several recording sensor placements prove that few-shot learning is a reliable technique even in real-world scenarios and gives valuable insights for developing an in-car emergency vehicle detection system.

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“…In this line, systems have been implemented to help elderly people at risk of falling, by estimating human orientation in three dimensions by monitoring the signals from different sensors [48]. Research has also been carried out on portable systems for the disabled, an example of which is a system that allows the characteristics of sounds to be estimated in real time and to create a fingerprint for people with hearing disabilities [49]. The IoT is becoming very popular even outside of its original home automation scenario, with music related apps popping up [50], along with rural development and smart farming [51].…”

Section: Community Analysismentioning

confidence: 99%

Electronics and Its Worldwide Research

et al. 2020

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The contributions of researchers at a global level in the journal Electronics in the period 2012–2020 are analyzed. The objective of this work is to establish a global vision of the issues published in the Electronic magazine and their importance, advances and developments that have been particularly relevant for subsequent research. The magazine has 15 thematic sections and a general one, with the programming of 385 special issues for 2020–2021. Using the Scopus database and bibliometric techniques, 2310 documents are obtained and distributed in 14 thematic communities. The communities that contribute to the greatest number of works are Power Electronics (20.13%), Embedded Computer Systems (13.59%) and Internet of Things and Machine Learning Systems (8.11%). A study of the publications by authors, affiliations, countries as well as the H index was undertaken. The 7561 authors analyzed are distributed in 87 countries, with China being the country of the majority (2407 authors), followed by South Korea (763 authors). The H-index of most authors (75.89%) ranges from 0 to 9, where the authors with the highest H-Index are from the United States, Denmark, Italy and India. The main publication format is the article (92.16%) and the review (5.84%). The magazine publishes topics in continuous development that will be further investigated and published in the near future in fields as varied as the transport sector, energy systems, the development of new broadband semiconductors, new modulation and control techniques, and more.

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Emergency Signal Classification for the Hearing Impaired using Multi-channel Convolutional Neural Network Architecture

Cited by 15 publications

References 9 publications

Urban Noise Analysis and Emergency Detection System using Lightweight End-to-End Convolutional Neural Network

Urban Noise Analysis and Emergency Detection System using Lightweight End-to-End Convolutional Neural Network

Few-Shot Emergency Siren Detection

Electronics and Its Worldwide Research

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