Highlights
What I built
- Waveform-based segment selection tied directly to analysis calls.
- Server-side BPM inference integrated into a lightweight UX.
- From Colab prototype to deployed app.
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Full-StackShipped
Count Coach
Dance practice tool with waveform selection and BPM analysis.
Built from a dance practice pain point: finding the right section and tempo quickly. The app combines waveform interactions with server-side tempo analysis.
- Date
- 2025-2026
- Signal
- Signal + Web
- Build stage
- Shipped and used as a personal practice tool
- Stack
- Next.js, WaveSurfer
signal-processingaudiofullstackdance
Demo
Project notes
Architecture
How the system works
- Client selects clip region and posts audio segment metadata.
- Backend processing runs tempo analysis and returns practice metrics.
- UI overlays tempo guidance for targeted repetition.
Challenges
What made it hard
- Syncing waveform selection with backend analysis boundaries.
- Balancing analysis latency with smooth interaction flow.
Lessons
What I learned
- Niche tools can be valuable if they remove repetitive friction.
- UX clarity matters as much as model/output quality.
Stack / materials
Next.jsWaveSurferLibrosaPythonVercel
- Future direction includes richer loop controls and movement-aware cues.
Media timeline
Build photos, clips, and process visuals. The goal is to show how the project evolved, not just the final screenshot.
Build snapshot
Iteration snapshot
Gallery 3