Music Player Based on Emotion Detection Using CNN

Arya, Rahul; Bhatt, Chandradeep; Mittal, Mudit

doi:10.1109/nkcon56289.2022.10126761

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…The emotion-based music player can be combined with the PyVLC media player to play music in real-time according to the detected emotions. [15] The PyVLC media player is a powerful media player library in Python that can play various types of media files and supports different video and audio codecs. By integrating the emotionbased music player with PyVLC, we can easily play the appropriate music file based on the emotions detected from the video input stream.…”

Section: Play Music Based On Emotionmentioning

confidence: 99%

Emotion-Based Music Player

Netravathi K S,

Bibi Haleema N,

Priyanka

et al. 2024

Int Res J Adv Engg Mgt

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Songs have always been a popular medium for communicating and understanding human emotions. Reliable emotion-based categorization systems can be quite helpful to us in understanding their relevance. However, the outcome of the research on Emotion-based music classification have not been the greatest. Here, we introduce EMP, a cross-platform emotional music player that play songs in accordance with the user’s feelings at the time. EMP provides intelligent mood-based music player by incorporating emotion context reasoning abilities into our adaptive music engine. EMP revolutionizes how users interact with music, fostering deeper connections between emotions and musical experiences. Our music player is composed of three modules: the emotion module, the classification module, and the queue-based module. The Emotion Module analyses a picture of the user’s face and uses the VGG16 algorithm to detect their mood with a precision exceeding 95%. The Music Classification Module gets an outstanding result by utilizing aural criteria while classifying music into 7 different mood groups. The queue module plays the songs directly from the mapped folders in the order they are stored, ensuring alignment with the user’s mood and preferences.

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Section: Play Music Based On Emotionmentioning

confidence: 99%