MERP: Music Emotion Recognition with Profile Information

54 full-length songs were dynamically rated for valence and arousal by listeners recruited via Amazon Mechanical Turk. The MERP dataset was created by amaai-lab for music emotion recognition research. The dataset page was last updated on 2026-06-20.

Use Cases

• Train models for continuous emotion prediction in music based on valence and arousal ratings.
• Study the subjectivity of emotional perception in music based on listener profile information.
• Benchmark music emotion recognition algorithms against human-annotated, full-length song data.

Strengths

• Contains 54 full-length songs, providing a basis for analysis on complete musical works.
• Features continuous, dynamic ratings for valence and arousal, capturing emotional changes over time.
• Data was collected via Amazon Mechanical Turk, likely providing a diverse pool of human raters.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last updated 2026-06-20; freshness should be verified.

Provenance

Source

amaai-lab

Collection Method

Collected via Amazon Mechanical Turk (MTurk).

Freshness

2026-06-20