Audio Understanding
What is happening in this sound? Can a machine identify structure, texture, dynamics, and intent in audio — not as metadata tags, but as measurable, verifiable properties?
Research
Can machines learn to hear?
This is the question Moodify exists to explore. Not as a slogan. As a research program.
The Question
Hearing is not the same as processing.
A system can process audio without understanding what happened in the sound.
Current audio systems treat sound as data to be moved, stored, and played. They measure file size, bit rate, sample rate. They do not ask: What is happening in this sound? What should a listener hear? How should this be played?
This is not a failure of existing systems. It is an opportunity for new ones.
Directions
Four research questions
Signal Analysis
What can we measure and how reliably? Beyond waveform and spectrum: phase relationships, temporal dynamics, spatial properties. What can be measured reproducibly across devices and conditions?
Perceptual Evaluation
What do listeners actually prefer? Not what they say they prefer in surveys — what they consistently choose when listening blind, repeatedly, in controlled conditions.
Playback Intelligence
How should this sound be played? Given understanding of the content, the device, the environment, and the listener — what playback parameters produce the best experience?
Method
Evidence before claims
Moodify research follows a strict protocol.
- Measurement first — What can be reliably measured?
- Reproducibility required — Can another system get the same result?
- Scope declared — What is the boundary of the claim?
- Limitation stated — What does this not prove?
- Human escalation — When does judgment require a person?
Research findings are published with version, scope, and maturity state. See evidence for current findings.
Current Work
What we are exploring now
Multi-scale auditory representation
Representing audio across time scales — from milliseconds to minutes — to capture both transient events and long-term structure.
Experimental · 2026Cross-machine measurement repeatability
Verifying that audio analysis produces identical results across different operating systems and Python versions.
Verified · 2026Algorithmic review with declared scope
Technical ranking within bounded, versioned rules — with explicit human escalation for out-of-scope cases.
Human-reviewed · 2026Boundaries
What we are not doing
- Not generating music with AI
- Not claiming to replace human judgment
- Not building a black-box quality score
- Not collecting private audio for training
- Not publishing unverified claims
Contact
Research inquiries
For research collaboration, media inquiries, or technical questions:
[email protected]