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Purchase Trance Dataset

"Trance" is an AI music dataset crafted to elevate machine-learning applications to new heights. This collection features a diverse selection of trance tracks paired with detailed metadata, providing an immersive journey into the pulsating rhythms and euphoric melodies of the trance genre.

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Dataset Specifications

Total Audio Tracks: Up to 100k Trance tracks
Type: Genre (Trance)
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 Trance Dataset is a comprehensive compilation of audio tracks paired with detailed metadata, curated to drive progress in machine learning across diverse applications. This dataset delves into the captivating realm of trance music, presenting a kaleidoscope of entrancing melodies, pulsating rhythms, and ethereal textures.

Within the Trance Dataset lies the distinct sonic landscape of trance music, characterized by its driving beats, hypnotic synthesizers, and uplifting harmonies. From sweeping arpeggios to throbbing basslines, and from intricate chord sequences to atmospheric layers, each component contributes to the distinctive sonic tapestry that defines trance music.

Complementing the audio tracks are comprehensive metadata annotations, offering insights into musical structure, instrumentation, key signatures, tempo variations, timestamps, and more. This metadata provides invaluable context for machine learning models to understand and interpret the complexities of trance music, enabling tasks such as generative music composition, music information retrieval (MIR), source separation, and beyond. The Trance Dataset emerges as a valuable asset for researchers, musicians, and developers alike, offering a gateway to explore the creative possibilities of trance music within the realm of machine learning, and paving the way for innovative advancements in computational musicology.

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