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There is an uncanniness in listening to a musical instrument you have never heard being played for the first time. As your brain makes sense of a new sound, it tries to frame it within the realm of familiarity, producing a tussle between the known and unknown.

The second album from Mexican-American producer Delia Beatriz, AKA Debit, embraces this dissonance. Taking the flutes of the ancient Mayan courts as her raw material and inspiration, Beatriz used archival recordings from the Mayan Studies Institute at the Universidad Nacional Autnoma de Mxico to create a digital library of their sounds. She then processed these ancient samples through a machine-learning program to create woozy, ambient soundscapes.

Since no written music has survived from the Mayan civilisation, Beatriz crafts a new language for these ancient wind instruments, straddling the electronic world of her 2017 debut Animus and the dilatory experimentalism of ambient music. The resulting 10 tracks make for a deliciously strange listening experience.

Opener 1st Day establishes the undulating tones that unify the record. They flutter like contemplative humming and veer from acoustic warmth to metallic note-bending. Each track is given a numbered day and time, as if documenting the passage of a ritual, and echoes resonate down the record: whistles appear like sirens during the moans of 1st Night and 3rd Night; snatches of birdsong are tucked between the reverb of 2nd Day and 5th Day.

The Long Count of the records title seems to express the linear passage of time itself, one replicated in the eternal, fluid flute tones. We hear in them the warmth of the human breath that first produced their sound, as well as Beatrizs electronic filtering that extends their notes until they imperceptibly bleed into one another and fuzz like keys on a synth. It is a startlingly original and enveloping sound that leaves us with that ineffable feeling: the past unearthed and made new once more.

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Debit: The Long Count review Mayans, machine learning and music - The Guardian