Duplicate from LaurenGurgiolo/Music_by_Emotion

Co-authored-by: Lauren Gurgiolo <LaurenGurgiolo@users.noreply.huggingface.co>

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README.md

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+---
+license: other
+license_name: hi
+license_link: LICENSE
+task_categories:
+- audio-classification
+language:
+- en
+tags:
+- music
+- emotion
+- recognition
+- classification
+pretty_name: Music by Emotion
+---
+🎵 Music by Emotion Dataset
+Dataset Summary
+
+The Music by Emotion dataset is a custom audio dataset designed for supervised music emotion recognition tasks. It consists of 1,000 music samples, each a 30-second audio clip, sourced from publicly available SoundCloud content.
+
+Each clip is labeled according to its emotional content, derived from musical characteristics such as tempo and musical key.
+
+🎯 Task
+
+Task type: Audio Classification
+
+Domain: Music Emotion Recognition
+
+Input: 30-second music audio clips
+
+Output: Emotion labels
+
+📂 Dataset Composition
+
+Total samples: 1,000
+
+Clip duration: 30 seconds
+
+Audio source: Publicly available SoundCloud tracks
+
+Format: Audio files suitable for machine learning pipelines
+
+🏷️ Labeling Methodology
+
+Emotion labels were assigned by analyzing two primary musical features:
+
+Tempo:
+
+Slow
+
+Medium
+
+Fast
+
+Musical key:
+
+Major
+
+Minor
+
+These musical attributes were used to infer the emotional characteristics of each clip.
+
+🎭 Emotion Classes
+
+The dataset includes the following 11 label categories:
+
+Label	Description
+Angry	High-intensity, aggressive musical characteristics
+Contempt	Dismissive or disdainful emotional tone
+Disgust	Harsh or unpleasant musical qualities
+Fear	Tense or suspenseful musical patterns
+Happy	Upbeat, positive emotional tone
+Neutral	Emotionally balanced or ambiguous content
+Sad	Slow, minor-key, melancholic music
+Sleepy	Low-energy, calming, or drowsy music
+Surprised	Sudden changes or unexpected musical elements
+Bad	Poor audio quality
+Boring	Incorrect or irrelevant format (e.g., podcasts, spoken content)
+
+Note:
+
+The Bad label indicates low-quality audio that may not be suitable for training models.
+
+The Boring label identifies non-music or incorrectly formatted content, such as podcasts.
+
+🔍 Intended Uses
+
+Music emotion classification
+
+Audio representation learning
+
+Benchmarking emotion recognition models
+
+Educational and research applications in music information retrieval (MIR)
+
+⚠️ Limitations
+
+Emotion labeling is inherently subjective.
+
+The dataset size (1,000 samples) may limit generalization for large-scale models.
+
+Labels are inferred from musical features rather than listener studies.
+
+Some samples are intentionally labeled as Bad or Boring, which may require filtering before training.
+
+📜 Licensing & Source Information
+
+Audio source: Publicly available SoundCloud content
+
+License: Users are responsible for ensuring compliance with SoundCloud’s terms of service and applicable licenses before redistribution or commercial use.
+
+🙌 Acknowledgements
+
+SoundCloud artists for publicly shared audio content
+
+Hugging Face for dataset hosting and tooling
+
+📬 Contact / Contributions
+
+Contributions, corrections, and improvements to labeling or metadata are welcome via pull requests.

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