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Purchase Alto Saxophone Dataset

Alto Saxophone is an AI music dataset curated to capture the melodies of the alto saxophone. It provides a collection of audio recordings and metadata to explore alto saxophone performances. It is great for generative music experiments, exploring MIR tasks, pioneering new techniques, and much more.

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Dataset Specifications

Total Audio Tracks: Up to 100k Alto Saxophone tracks
Type: Instrument (Alto Saxophone)
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 Alto Saxophone Dataset is an extensive compilation of audio tracks paired with intricate metadata, thoughtfully crafted to drive progress in machine learning applications across diverse fields. This dataset offers an intriguing journey into the domain of alto saxophone music, revealing the instrument's distinct tonality, expressive capabilities, and adaptable melodies.

From mellow jazz tunes to spirited solos, the alto saxophone boasts a versatile range suited to various musical genres. Each track in this dataset encapsulates the emotional depth and unique characteristics of the alto saxophone, providing a rich tapestry of material for analysis and creative exploration.

Accompanying the audio recordings are detailed metadata annotations, providing insights into musical structures, instrumentation, key signatures, tempo fluctuations, timestamps, and more. This comprehensive metadata empowers machine learning models to discern the intricacies of alto saxophone performances, facilitating tasks such as generative music composition, music information retrieval (MIR), source separation, and beyond. The Alto Saxophone Dataset serves as an invaluable resource for researchers, musicians, and developers eager to leverage the expressive potential of the alto saxophone within the realm of machine learning, fostering innovative approaches to music synthesis, analysis, and interpretation.

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