Audio-Visual Model for Generating Eating Sounds Using Food ASMR Videos

Uchiyama, Kodai; Kawamoto, Kazuhiko

doi:10.1109/access.2021.3069267

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…On the Neurosciences front, some notable works include Ohta & Inagaki (2021), Gyoung (2021), Maniago et al (2021), Idayati et al (2021), Fredborg et al (2021), Lee et al (2020), Smith et al (2020), Paszkiel et al (2020), Valtakari et al (2019), Poerio et al (2018), Lochte et al (2018), Barratt et al (2017), andSmith et al (2017). The Sonology field has also produced some recent works on ASMR, most notably Uchiyama & Kawamoto (2021), Klefeker et al (2020), Fukushima (2019), Kovacevich & Huron (2018), Janik & Banissy (2018), and Rouw & Erfanian (2018).…”

Section: What Is Asmr and How Is It Linkedmentioning

confidence: 99%

YouTube, the Attention Economy and Digital Audience Interest in Sponsored ASMR Videos

Ruiz

2022

TAJ

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Si bien el fenómeno de videos ASMR (siglas del inglés Autonomous Sensory Meridian Response) en YouTube ha tenido un aumento notable en México en los últimos años, los principales canales aún carecen de uso efectivo y continuo de estrategias y tácticas de marketing digital como se observa en los videos patrocinados, a diferencia de los canales más populares de otras latitudes, esencialmente angloparlantes. Mediante una revisión detallada en YouTube, este trabajo compara ambos materiales y argumenta que el mercado mexicano puede aprovechar las estrategias de marketing de sus contrapartes, con algunas modificaciones, para montarse a la economía de la atención, aquella en donde la cognición se plantea como un bien que puede ser monetizado. El análisis de ambos grupos de canales halló que, en general, aquellos perfiles administrados por creadores de ASMR mexicanos cuentan con menor calidad de producción, escaso conocimiento de conceptos básicos de marketing y menor frecuencia de publicación de videos en inglés, al contrario de los canales más populares a nivel internacional. En el texto se discuten estos hallazgos a la luz de los conceptos de marketing relativos al alcance, relevancia y resonancia, así como del constructo de la economía de la atención.

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Section: What Is Asmr and How Is It Linkedmentioning

confidence: 99%

YouTube, the Attention Economy and Digital Audience Interest in Sponsored ASMR Videos

Ruiz

2022

TAJ

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Automated detection and recognition system for chewable food items using advanced deep learning models

Kumar,

Koul,

Kamini

et al. 2024

Sci Rep

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Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.

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Exploring the Power of Deep Learning for Seamless Background Audio Generation in Videos

Nambiar,

Likhita,

Pujya

et al. 2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Audio-Visual Model for Generating Eating Sounds Using Food ASMR Videos

Cited by 5 publications

References 17 publications

YouTube, the Attention Economy and Digital Audience Interest in Sponsored ASMR Videos

YouTube, the Attention Economy and Digital Audience Interest in Sponsored ASMR Videos

Automated detection and recognition system for chewable food items using advanced deep learning models

Exploring the Power of Deep Learning for Seamless Background Audio Generation in Videos

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