Emousic: Emotion and Activity-Based Music Player Using Machine Learning

Sarda, Pranav; Halasawade, Sushmita; Padmawar, Anuja; Aghav, Jagannath

doi:10.1007/978-981-13-6861-5_16

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…Certain systems rely on centralized servers for data storage and processing [1][2][3][4][5][6][7]9], raising privacy concerns. The decentralized blockchain backbone provides robust encryption, safeguarding user data.…”

Section: Robust Data Privacy Protectionmentioning

confidence: 99%

“…The Internet of Things (IoT) has revolutionized numerous industries, including music therapy [1,2], personalized music recommendation systems [3][4][5], and exercise and fitness [6][7][8][9]. Several studies have demonstrated the positive effects of music on exercise performance, mood elevation, and fatigue reduction, such as improved endurance and better adherence to exercise programs [10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Running Exercise With IoT, Blockchain, and Heart Rate Adaptive Running Music

Chen,

Tang

et al. 2024

IEEE Access

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This research proposes a music-adaptive IoT exercise system integrating edge computing and blockchain technology to enhance middle-distance running exercise. The system personalizes music tempo based on real-time biosignals to provide a tailored experience. Experiments demonstrate that adaptive music significantly augments users' physiological, psychological, and behavioral responses compared to no music, improving engagement and effectiveness. The system overcomes limitations in existing solutions regarding personalization, real-time adaptation, and data privacy. Edge computing enables on-device processing, and blockchain encryption guarantees security. Results highlight the benefits of HR adaptive running music for arousal, pleasure, and performance. This study offers insights into designing personalized, rewarding exercise systems using emerging technologies. It has promising implications for exercise, rehabilitation, and recreational fitness. Further work should investigate specific mechanisms and long-term impacts, integrate additional signals and music parameters, and enhance security. In conclusion, this innovative system combines IoT, adaptive music, edge computing, and blockchain to unlock new possibilities in exercise experiences.

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Section: Robust Data Privacy Protectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Running Exercise With IoT, Blockchain, and Heart Rate Adaptive Running Music

Chen,

Tang

et al. 2024

IEEE Access

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“…Sarda et. al Sarda et al (2019) have presented an emotion driven music recommendation system named as Emousic using machine learning algorithms. The model is trained using Random Forest classifier through bootstrap aggregating or bagging to improve the accuracy of the generated music playlist MOODSIC -A MOOD BASED MUSIC PLAYER based on user mood.…”

Section: Related Workmentioning

confidence: 99%

“…Through their study, Sarda et al Sarda et al (2019) propose a new personalized affective music playlist generation system. Their focus is on mood statistically inferred from data sources viz., text, audio, image and sensors.…”

Section: Related Workmentioning

confidence: 99%

Moodsic - A Mood Based Music Player

Rawat

Jothi

Vaishnavi

et al. 2023

Preprint

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A large amount of music data is now available on the Internet thanks to the advent of the World Wide Web. In addition to seeking for expected music for clients, it becomes necessary to build a recommendation service. The majority of existing music recommendation systems relies on collaborative or content-based engines. However, a user's music selection is not only based on prior preferences or musical content. However, it is also reliant on the user's mood. This paper presents an mood-based music (MoodSIC) recommendation framework that automatically learns a user's mood and suggest a list of songs pertaining to the mood. MoodSIC first detects the listener’s mood using various parameters such as skin temperature, facial texture, voice input, facial expression and then render a recommendation of the songs. The MoodSIC system is delivered as a web-application which uses MongoDB as a back-end for storing the songs. The proposed recommendation system provides a user-friendly interface to detect user mood using webcam, generate recommended playlist and autoplay music to the liking from generated playlist.

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“…The other approach, however, exploits the user activity data and involves no expensive data acquisition sessions, which is regarded as more promising. Activities like the operations and interactions on music systems can be used to represent user emotion states [46,47]. Activities on social media are a fruitful source of user emotion state representations.…”

Section: Related Workmentioning

confidence: 99%

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Wang

Ding

et al. 2021

Electronics

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Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of Precision, Recall, F1, and HitRate than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms.

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Emousic: Emotion and Activity-Based Music Player Using Machine Learning

Cited by 9 publications

References 10 publications

Enhancing Running Exercise With IoT, Blockchain, and Heart Rate Adaptive Running Music

Enhancing Running Exercise With IoT, Blockchain, and Heart Rate Adaptive Running Music

Moodsic - A Mood Based Music Player

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

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