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Indie Pop dataset for AI-Generated Music

Indie music, noted for its independent and eclectic nature, provides a particular flavor to this dataset. The genre's catchy melodies, straightforward song structures, and raw, unfiltered production approach make it an appealing option for generative AI music projects.

Our Indie Pop Dataset offers a diverse playground. Isolate individual parts inside a recording, separate voices from instrumentation, and watch machine learning unravel the layers that define indie pop's unique sound. The genre's dynamic range and varied instrumentation make it an ideal environment for developing source separation algorithms.

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Dataset Highlights


Genre-specific Diversity

The dataset includes a wide range of indie pop sub-genres, capturing the essence of dream pop, folk-pop, electro-pop, and more. This diversity enables AI models to understand and reproduce the nuances characteristic of indie pop's eclectic sound.

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Rich Audio Samples

High-quality audio samples in various formats provide a foundation for training models to generate realistic and sonically pleasing indie pop tracks. The dataset includes professionally produced instrumentals, vocals, and full tracks to enhance the learning experience for AI systems.


MIDI Files for Musical Structure

MIDI files are included to provide insight into the underlying musical structure of indie pop compositions. This allows AI models to understand the arrangement, harmony, and melodic patterns commonly found in indie pop music, facilitating the creation of more authentic compositions.


Metadata for Contextual Understanding

Detailed metadata accompanies each track, offering information on key, tempo, time signature, and other relevant details. This metadata aids AI models in understanding the contextual elements of each piece, leading to more contextually coherent and musically accurate generated content.

Indie Pop

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