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Explainable Convolutional Neural Network with Facial Emotion Enabled Music Recommender System

Published: 30 May 2023 Publication History

Abstract

In the modern world, there is a lot of stress and exhaustion, which increases hopelessness and poor health. Medical research shows that listening to music that matches one's mood can be uplifting. A music listener finds it very difficult to choose what music to listen to from the vast and remarkable array of selections that are currently available. According on the user's mood, there have been a number of suggestions for the applications that are available for these issues, such as music, dining, and shopping. One industry where there is a significant opportunity to provide customers a wide range of options based on their preferences and other data is music. It is well understood that humans use face expressions to express what they want to say and the context in which they want to say. face is one of the most important organs in bodies. The emotions of an individual can be approximated to a certain degree of precision by using certain traits discernible on the face. With the development of technology, it is now possible to use a camera to recover recognizable facial traits as inputs. Songs from a personalized playlist are played while the mood is being determined using the data collected. More than 50% users feel that at some point, the number of tracks in their library is so huge that they were unable to play the song which they really desired to play. This removes the time-consuming process of manually choosing music or changing playlists and enables the building of a playlist that is suitable for the individual based on their emotional state or mood. In order to develop an automated playlist generation approach that uses emotion recognition to recommend track, a number of algorithms will be examined. The application is built in a way that will allow user to play tracks based on his facial expression.

References

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ICIMMI '22: Proceedings of the 4th International Conference on Information Management & Machine Intelligence
December 2022
749 pages
ISBN:9781450399937
DOI:10.1145/3590837
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 30 May 2023

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  1. CCS Concepts •Computing methodologies,Machine learning algorithms,Feature selection

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