Hi r/androiddev,

I’m sharing an open-source project I’ve been working on that combines on-device ML, native audio DSP, and Android’s global audio effect API to dynamically optimize sound in real time — for any audio source (Spotify, YouTube, games, calls, etc.).

🎯 What it does

• Applies adaptive equalization based on a lightweight neural model (Tiny AutoFUS, 25 KB)
• Works globally — no need to modify individual apps
• Runs 100% offline on device (no cloud, no internet)
• Built with Kotlin + JNI + C++ (Biquad filters, FFT, noise-aware gain)

🛠️ Tech stack

• ML: PyTorch Mobile (Lite) — model loaded from assets
• Native: CMake + NDK — core DSP in jni/core/ (BiquadEQ, FFTProcessor, NoiseGate)
• Android APIs: AudioEffect, AudioCaptureService, MODIFY_AUDIO_SETTINGS
• Architecture: Hybrid — Kotlin for control, C++ for low-latency processing

🔗 GitHub

https://github.com/Kretski/audio-optimizer-android

Includes:

• Full source (Kotlin + C++)
• Prebuilt APK (v1.0)
• MIT license

❓ Why share this?

I’d love feedback on:

• Best practices for global audio effects (stability across OEMs?)
• Efficient Tensor ↔ JNI data transfer for real-time inference
• Ideas for latency reduction (currently ~20–40ms on mid-range devices)

This is part of a larger effort around edge AI for scientific & industrial applications (think drone acoustics, engine diagnostics), but the audio module is general-purpose.

Thanks for taking a look!