SyncPixel: A Computer Vision Framework for Automated Emotion-Based Music Suggestions Article Swipe
Aman, Sanskar Gaherwal, Sarthak Sharma, Dhruv Goyal, Divy Raj
·
YOU?
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· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17832363
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17832363
In the age of social media, visual content plays a dominant role in digital self-expression. However, selecting the right accompanying music to match the emotional tone of an image remains a time-consuming and subjective process for users. This paper presents an intelligent system that automates this task by analyzing images to generate emotion-based song recommendations. The proposed framework leverages computer vision techniques to extract visual and contextual cues such as facial expressions, background scenery, lighting conditions, and color tone. These features are then mapped to emotional states using deep learning models, forming the basis for music recommendation through emotion–music correlation analysis. By integrating APIs such as Spotify or YouTube Music, the system curates song lists that align with the detected emotion, enhancing user experience and reducing decision fatigue. Experimental results demonstrate that the model effectively bridges visual emotion recognition and audio recommendation, offering a novel, AI-driven solution for personalized multimedia pairing in social media applications. Keywords Image Emotion Recognition, Computer Vision, Facial Expression Analysis, Deep Learning, Emotion Detection, Music Recommendation System, Affective Computing, Spotify API, Scene Analysis, Artificial Intelligence, Multimodal Emotion Recognition.
Related Topics
Concepts
Computer science
Process (computing)
Task (project management)
Facial expression
Emotion recognition
Human–computer interaction
Artificial intelligence
Multimedia
Deep learning
Affective computing
Emotion detection
Face (sociological concept)
Image editing
Tone (literature)
Facial recognition system
Emotion classification
Computer vision
Task analysis
Emotional expression
Recommender system
Speech recognition
Image (mathematics)
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.5281/zenodo.17832363
- OA Status
- green
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W7109137777Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17832363Digital Object Identifier
- Title
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SyncPixel: A Computer Vision Framework for Automated Emotion-Based Music SuggestionsWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-19Full publication date if available
- Authors
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Aman, Sanskar Gaherwal, Sarthak Sharma, Dhruv Goyal, Divy RajList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17832363Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17832363Direct OA link when available
- Concepts
-
Computer science, Process (computing), Task (project management), Facial expression, Emotion recognition, Human–computer interaction, Artificial intelligence, Multimedia, Deep learning, Affective computing, Emotion detection, Face (sociological concept), Image editing, Tone (literature), Facial recognition system, Emotion classification, Computer vision, Task analysis, Emotional expression, Recommender system, Speech recognition, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.applications. | 155 |
| abstract_inverted_index.emotion-based | 53 |
| abstract_inverted_index.Recommendation | 170 |
| abstract_inverted_index.recommendation | 97 |
| abstract_inverted_index.time-consuming | 32 |
| abstract_inverted_index.emotion–music | 99 |
| abstract_inverted_index.recommendation, | 142 |
| abstract_inverted_index.recommendations. | 55 |
| abstract_inverted_index.self-expression. | 15 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile.value | 0.81803101 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |