ABOUT
Can machines learn to hear?
Moodify exists because generating sound and hearing sound are different capabilities. A system that produces audio is not thereby able to understand what happened, judge it responsibly, verify an intervention, or learn from evidence.
THESIS
Research origin
Moodify asks whether machines can learn to hear. That research remains inside the system so the public product can stay focused on listening and Play.
WSEWhat happened in the sound — waveform, spectrum, loudness, dynamics, phase.
MSEWhat is the musical structure — beat, tempo, sections, roles.
PPEHow the result is produced, verified, and recovered reliably.
BOUNDARIES
What Moodify is not
| Not | Because |
|---|---|
| An automatic-mastering product | Hearing before intervention; no unstated change. |
| A preset browser or black-box quality score | Evidence before claims; findings are inspectable. |
| A machine with unlimited final authority | Human authority where required; escalation is first-class. |
| A generic AI music feed | Works and creators first; no engagement-only hierarchy. |
PRINCIPLES
Shared promises across every surface
Hearing before interventionDon't change sound without a stated reason.
Evidence before claimsDon't call a result better without inspectable support.
Uncertainty before false confidenceState limits, missing evidence, and unresolved judgment.
Human authority where requiredMachine judgment decides only inside an approved scope.
Traceability before convenienceImportant state changes and claims are attributable and recoverable.
Learning before feature accumulationA case should leave reusable knowledge, not only an output file.