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🎮 Turing's Frequency — A Rhythm Game Where You Decrypt the Voices of History

🏆 This is a submission for the June Solstice Game Jam 🎯 What I Built Turing's Frequency is a browser-based rhythm game where you decrypt encrypted radio signals by listening to musical patterns and recreating them. Each signal carries a message from a historical figure who changed the world — voices that were silenced, ignored, or forgotten, now restored through your rhythm. 🎮 👉 PLAY THE GAME LIVE 👈 📖 The Story The game is set in 1954 , on the desk of Alan Turing at the University of Manchester. A radio crackles with fragmented transmissions — encrypted messages carrying words of Pride , resistance , and identity . You are a student who has found Turing's last notebook, and with it, the key to decrypting these signals. 🌅 The connection to the June solstice: As you decrypt each signal, the screen literally brightens — from near-darkness to a flood of golden light. The solstice is the moment light and dark trade places, and this game makes that transition tangible. 🎬 Video Demo 👆 Watch the full gameplay loop: title → story → rhythm gameplay → decrypted messages → victory screen with solstice light effect. 🕹️ How to Play Key Action 1 2 3 4 Play notes ↑ ↓ ← → Arrow keys (alternative) Space / Enter Advance screens 🎧 Listen to the signal pattern 🎹 Repeat the notes in order 🔓 Decrypt the message 🌅 Restore the voice 💻 The Code The entire game is a single HTML file (~32KB) with zero external dependencies . No frameworks, no libraries, no asset files — just HTML, CSS, and vanilla JavaScript. mamoor123 / turings-frequency Turing's Frequency - A Rhythm of Light. June Solstice Game Jam 2026 entry. ⚡ Key Technical Decisions 🔊 Web Audio API for all sound: Every tone is synthesized in real-time using oscillators. The game uses a pentatonic scale (C4, E4, G4, C5) so every combination of notes sounds pleasant. No audio files needed. function playTone ( freq , duration = 0.3 , type = ' sine ' , volume = 0.3 ) { const osc = audioCtx . createOscillator (); const gain = audioCtx . create

2026-06-09 原文 →
AI 资讯

Qtractor Complete Guide

Complete First-Time Setup Guide for Qtractor on Ubuntu 26.04 install and prepare the system verify audio works configure PipeWire/JACK configure Qtractor record audio record while playing other tracks export projects tune latency troubleshoot problems Qtractor is a lightweight Linux DAW (Digital Audio Workstation) for audio and MIDI recording. On Ubuntu, most problems come from: JACK / PipeWire / ALSA conflicts Permissions Wrong audio device selection Monitoring setup Sample-rate mismatches USB devices reconnecting Tracks not armed correctly This guide walks through a strategic / systematic / stable setup from zero, and covers the common failure cases along the way. PART 1 QUICK START SETUP 1. Understanding the Linux Audio Stack Modern Ubuntu audio typically works like this: Applications PipeWire JACK compatibility layer ALSA drivers Audio hardware For Qtractor, the recommended setup is: PipeWire enabled JACK compatibility enabled Qtractor using JACK mode through PipeWire 2. Install the Required Packages Install everything needed: sudo apt update sudo apt install \ qtractor \ pipewire \ pipewire-audio \ pipewire-pulse \ pipewire-jack \ wireplumber \ qpwgraph \ helvum \ pavucontrol \ alsa-utils \ jackd2 \ qjackctl \ ffmpeg Useful tools: Tool Purpose qtractor DAW qjackctl JACK control panel qpwgraph audio routing graph helvum simpler routing pavucontrol audio device management alsa-utils microphone troubleshooting 3. Reboot After installation: reboot This ensures all audio services start cleanly. 4. Verify the Audio System Is Running Check PipeWire: systemctl --user status pipewire Check WirePlumber: systemctl --user status wireplumber Check Pulse compatibility: systemctl --user status pipewire-pulse You should see: active (running) 5. Verify Audio Devices Exist Check Playback Devices aplay -l Check Recording Devices arecord -l Check PipeWire Audio Nodes wpctl status You should see sections like: Audio Devices Sinks Sources Typical onboard audio may appear as: Built-i

2026-06-04 原文 →
AI 资讯

Clean Audio Before Whisper: How Noise Removal Improves Transcription Accuracy (With Code)

Whisper is remarkably robust. But "robust" doesn't mean "immune to noise." If you've ever run a meeting recording through Whisper and gotten back garbage — or worse, confidently wrong text — the problem is usually the audio, not the model. Here's the thing: different noise types fail differently. Electrical hum causes Whisper to hallucinate syllables. Echo makes it drop words entirely. Static makes it confuse phonemes. Knowing which noise you have tells you exactly which fix to apply. This post covers: ✅ How each noise type (hum, hiss, echo, wind, static) degrades Whisper output ✅ A Python preprocessing pipeline that detects and removes noise before transcription ✅ How to call the StemSplit Denoise API for cloud GPU noise removal (no local setup) ✅ Measured WER improvements you can reproduce The Noise → Transcription Failure Map Noise Type What It Sounds Like How It Breaks Whisper Hum (50/60 Hz) Constant low-frequency "buzz" Inserts phantom syllables, lowers confidence Hiss High-frequency "shhh" Loses sibilants, confuses "s/sh/f" sounds Echo / Room reverb Words "bounce" and overlap Drops end-of-sentence words, merges phrases Wind Burst plosives, low-frequency rumble Transcribes as "[inaudible]", breaks sentence segmentation Static / crackling Random pops and snaps Breaks word boundaries, causes mid-word cuts These aren't hypothetical. They're reproducible failure modes. Let me show you how to handle each one. Prerequisites pip install openai-whisper requests python-dotenv soundfile numpy librosa You'll need: A StemSplit API key from stemsplit.io/developers (free 5-minute tier, no credit card) ffmpeg installed ( brew install ffmpeg / sudo apt install ffmpeg ) The Preprocessing Pipeline Here's the full pipeline before we break it down: Audio file → [Noise detection] → [Denoise via StemSplit API] → [Post-process: normalize, trim silence] → Whisper → Transcript Step 1: Detect What Kind of Noise You Have Before throwing everything at a denoiser, it helps to know what you

2026-06-02 原文 →
AI 资讯

Server-Side WebRTC Noise Reduction with Pion, FFmpeg, and RNN Models

This is a sanitized engineering note about server-side audio noise reduction for WebRTC calls. Source article: https://www.lodan.me/posts/server-side-webrtc-noise-reduction-pion-ffmpeg-rnn/ What the prototype tests The goal is not to replace WebRTC's built-in audio processing. The narrower test is: receive a WebRTC Opus track with Pion read RTP packets in OnTrack decode Opus payloads to PCM pipe raw PCM into FFmpeg apply the arnndn RNN noise reduction filter validate the output as a file before considering real-time forwarding Why this boundary matters RTP, Opus, PCM, and FFmpeg raw audio input are different boundaries. If the PCM format is wrong, FFmpeg may still produce a file, but the result should not be trusted. For example, if the Go side writes int16 PCM, the FFmpeg input format should be reviewed as s16le , not casually treated as s32le . Production concerns The prototype is useful because it isolates the audio path, but production use needs more work: buffering and latency CPU and memory isolation FFmpeg process lifecycle model choice packet loss and jitter RTP timestamps audio/video sync whether the processed audio is returned to WebRTC or only recorded The full article has diagrams and the longer explanation: https://www.lodan.me/posts/server-side-webrtc-noise-reduction-pion-ffmpeg-rnn/

2026-05-30 原文 →
AI 资讯

This sound card could give gamers a competitive edge

Fosi Audio announced a new sound card today with a unique feature designed to give FPS players an advantage. The C3 Gaming Sound Card, which sits outside your PC or laptop and connects with a USB-C cable, includes the company's StepSense "audio enhancement technology" powered by a model that was "trained on extensive FPS audio […]

2026-05-28 原文 →