Implementation of Audio Event Recognition for The Elderly Home Support Using Convolutional Neural Networks

Ramadhan, Alif Wicaksana; Wijayanto, Ardik; Oktavianto, Hary

doi:10.1109/ies50839.2020.9231702

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…Correct perception and recognition of sound have been made delivered using KNNs classifier and audio fingerprinting. Later, Ramadhan and team [227] published their work in which acoustic event recognition is investigated as part of a smart home system for elderly people. In their work, spectrograms are extracted from practically collected audio data to train a DL model i.e.…”

Section: Healthcare Applicationsmentioning

confidence: 99%

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

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Section: Healthcare Applicationsmentioning

confidence: 99%

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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“…Separac ¸ão ou Segmentac ¸ão de Áudio -Audio Separation and Segmentation é separar ou isolar algum som específico. Por exemplo, no meio de outros ruídos, separar a voz de uma pessoa específica, ou no meio de um musica, separar o som apenas de um instrumento 17 . Por exemplo, ouvir e isolar o som de um corac ¸ão humano ou da respirac ¸ão para identificar anomalias, como em [60] ou [61].…”

Section: E Classificac ¸ãO De áUdiounclassified

“…A análise de dados de áudio trata da compreensão dos sinais de áudio capturados por dispositivos digitais, com inúmeras aplicac ¸ões seja na indústria, saúde, produtividade e cidades inteligentes -Smart Cities. As aplicac ¸ões incluem análise de satisfac ¸ão do cliente a partir de chamadas, auxílio no diagnóstico médico, monitoramento de paciente com tecnologias assistivas para pessoas idosas ou par amelhoria de qualidade de vida [1]- [8], análise de áudio para seguranc ¸a pública ou planejamento na tomada de decisões contexto de cidades inteligentes [9]- [17], análise sonora de defeitos em teste de qualidade na indústria [18]- [20], monitoramento não invasivo de florestas [21]- [27] identificando conflitos com humanos e vida silvestre -Human-wildlife conflict (HWC) [28] (armas de fogo, motosserras, presenc ¸a de animais perigosos...) [29] ou análise da vocalizac ¸ão de animais (ecoacústica [30] ), acompanhando migrac ¸ão de pássaros, sons subaquáticos de baleias [31], identificando presenc ¸ão de alguma espécie na recuperac ¸ão de florestas, entre diversas outras aplicac ¸ões. O smart audio [32], permite interpretar o som, acoustic sensing, produzindo dados relevantes para tomadas de decisão e pode ser aplicado ou trazer contribuic ¸ões em diversos campos.…”

Section: Introduc ¸ãOunclassified

Um Estudo sobre Técnicas utilizadas para o Reconhecimento de Sons com o uso de Inteligência Artificial e Python

Dörr¹,

Aylon²

2022

Anais Do XIX Congresso Latino-Americano De Software Livre E Tecnologias Abertas (Latinoware 2022)

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“…Human care robots and smart homes need to recognize sounds occurring in the users' private space in real time and at all times without any off time to provide services to the users. So, human care robots and smart homes store the incoming sound data in real time as audio files with a certain duration using microphones, and they send the audio files to the server for data pre-processing and event recognition [32][33][34][35]. This system enables the robots and the smart homes to recognize sound events in real time and automatically.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Sound Recognition System for Human Care Robot Considering Custom Sound Events

Kim,

Nam,

Choi

et al. 2024

IEEE Access

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In real-life situations where human care robots are deployed, there are custom sound events whose acoustic characteristics change depending on the user's choice unlike general sound events so that the human care robots cannot recognize custom sound events correctly in a conventional way. To solve this critical problem, a real-time sound event recognition system with customization process is proposed. The human care robot collects custom sound samples of a specific user and customizes a sound event recognition model. The overfitting-based customized model shows the best recognition performance by improving F-scores by 66.4% on average compared to the conventional recognition model. After the customization process, the human care robot performs a real-time sound recognition by consistently streaming robot's real-time microphone signals into the overfitting-based customized SER model. In this process, an optimized overlap is applied on subsequent audio inputs on SER to achieve sufficiently fast response and robust performance. As a pilot test of the human care robot implemented in actual environment, the real-time sound recognition system shows the best average F-score of 0.982 with 75% overlap for sound events including custom sounds. This pilot test result confirms that the real-time sound recognition system with customization process can be successfully applied to human care robots to respond to the custom sounds.INDEX TERMS Sound event recognition, human care robot, custom sound event, real-time system

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Implementation of Audio Event Recognition for The Elderly Home Support Using Convolutional Neural Networks

Cited by 4 publications

References 9 publications

A Survey on Artificial Intelligence-Based Acoustic Source Identification

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Um Estudo sobre Técnicas utilizadas para o Reconhecimento de Sons com o uso de Inteligência Artificial e Python

Real-Time Sound Recognition System for Human Care Robot Considering Custom Sound Events

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