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Purchase Sitar Dataset

"Sitar" is an AI music dataset curated for delving into generative music experiments, exploring MIR tasks, or pioneering new techniques in traditional instrument processing. It provides a rich repository of audio recordings and metadata to explore the intricacies of sitar performances.

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Dataset Specifications

Total Audio Tracks: Up to 100k Sitar tracks
Type: Instrument (Sitar)
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 Sitar Dataset, a comprehensive repository of audio tracks accompanied by detailed metadata, compiled to drive advancements in machine learning applications across various domains. This dataset offers a systematic exploration into the intricate world of sitar music, showcasing the instrument's distinctive timbre, melodic intricacies, and cultural significance.

Originating from the Indian subcontinent, the sitar has evolved into a prominent instrument in classical Indian music and beyond. Each audio track within this dataset captures the nuanced nuances and expressive qualities of the sitar, providing a diverse array of material for analysis and research purposes.

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 sitar performances, facilitating tasks such as generative music composition, music information retrieval (MIR), source separation, and beyond. The Sitar Dataset serves as a valuable resource for researchers, musicians, and developers seeking to explore the unique characteristics of the sitar within the realm of machine learning, enabling innovative approaches to music synthesis, analysis, and interpretation.

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