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Purchase Marimba Dataset

The Marimba dataset is an AI music dataset curated to capture the resonant and melodious tones of the marimba. From rhythmic ostinatos to intricate melodic lines, this collection offers a versatile array of marimba performances to enrich compositions across various musical genres and styles.

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Dataset Specifications

Total Audio Tracks: Up to 100k Marimba tracks
Type: Instrument (Marimba)
File Format: WAV, FLAC, MP3, CSV, JSON

Dataset includes:

  • Duration

  • Key

  • Tempo

  • BPM Range

  • Mood

  • Energy

  • Description

  • Keywords

  • Chord Progressions

  • Timestamps

  • Time Signature

  • Number of Bars

Purchasing License

Annual License
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Perpetual License
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Free audio sample available

The Marimba Dataset is a comprehensive collection of audio tracks accompanied by detailed metadata, curated to facilitate advancements in machine learning across diverse applications. This dataset offers an extensive exploration into the realm of marimba music, showcasing the instrument's distinct tonal characteristics, rhythmic complexities, and expressive potential.

Originating from Africa and Central America, the marimba has evolved into a prominent percussion instrument with a wide range of musical applications. Each audio track within this dataset captures the nuanced nuances and emotive qualities of the marimba, providing a diverse array of material for analysis and research endeavors.

Accompanying the audio recordings are detailed metadata annotations, offering insights into musical structures, instrumentation, key signatures, tempo variations, timestamps, and other pertinent details. This comprehensive metadata empowers machine learning models to discern the intricacies of marimba performances, facilitating tasks such as generative music composition, music information retrieval (MIR), source separation, and beyond. The Marimba Dataset serves as a valuable resource for researchers, musicians, and developers seeking to explore the unique characteristics of the marimba within the realm of machine learning, enabling innovative approaches to music synthesis, analysis, and interpretation.

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