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• W4312790777 abstract "Automated audio captioning (AAC), is generating informative text information from the audio scene. It acts as a modality to understand the environment context. In domestic environment such as home, many audio signals provide meaningful information. Appliance’s sound, pet’s sound, baby’s activities and so on. Sound event detection systems understand the sound events. The accuracy of detection is affected, by overlapping sounds and increase in number of sound classes in domestic environment. Sound event detection also do not provide information about the sequence of events. A novel way is to understand the sequence of sound events is by generating captions from it. A textual summary of the sound events. Then using natural language processing (NLP) to provide context information to artificial intelligence systems. This paper proposes an automated audio caption engine, designed for domestic environment. To increase the accuracy, a web framework to collect the domestic environment data and captions is proposed. The paper discusses the convolutional neural network (CNN) based encoder to understand audio, and long short-term memory (LSTM) based decoder network to generate textual representation. Paper provides experimental results and comparison with state-of-the-art solutions." @default.
• W4312790777 created "2023-01-05" @default.
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• W4312790777 date "2022-10-03" @default.
• W4312790777 modified "2023-10-18" @default.
• W4312790777 title "Web Framework for Enhancing Automated Audio Captioning Performance for Domestic Environment" @default.
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• W4312790777 doi "https://doi.org/10.1109/icccnt54827.2022.9984255" @default.
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