How Computers Hear Practice: Pitch, Timing, and Note Feedback
How computers listen to a played or sung melody, compare it to a target exercise, and return a score plus per-note feedback on pitch and timing
This series walks through how a practice engine scores a sung or played melody against a target exercise. You will learn how sound is prepared and converted into MIDI, how probabilistic pitch tracking finds a note over time, how notes are grouped and aligned so extra or missed notes are not graded as pitch mistakes, how pitch and timing scores use perceptual thresholds instead of statistical outliers, and how per-note feedback can stay specific without demotivating false alarms.
Episodes
- 1
Sound, MIDI, and a Melody You Can Grade
Practice asks a sharper question than harmony or rhythm: did you play the right notes at the right times? Discover why a computer grades pitch in MIDI, where one step is always one semitone.
- 2
Finding Pitch Over Time – pYIN and Voiced Notes
A waveform does not name a note. Meet pYIN: a pitch tracker that estimates a fundamental every few milliseconds, then decides when a real note is present.
- 3
From Frames to Notes – Segmentation and Alignment
Pitch-per-frame is not a melody yet. Group frames into notes, then align them to the target so an extra or missed note is not graded as the wrong pitch.
- 4
Scoring Pitch and Timing – Thresholds with Musical Meaning
Each aligned note gets pitch credit and timing credit with public thresholds. Combined, pitch carries 70 percent of the grade because the wrong note is a deeper mistake than a slightly off beat.
- 5
Feedback You Can Trust – Known Errors and Encouragement
The last step is language: one specific line per note, conservative about the word wrong. A take with planted mistakes shows whether those verdicts can be trusted.
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Burak Gündüz
Creative Coder
- Episodes
- 5
- Cadence
- Weekly
- Subscribers
- 0
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