标签：#Audio

Marshall announced new versions of its Acton and Stanmore Bluetooth speakers today with upgraded tweeters, bass ports, and internal designs that improve their ability to fill a room with sound. Both the Acton IV and Stanmore IV replace their four-year-old predecessors with a new focus on repairability. Parts including knobs, feet, and the speakers' front […]

Nothing announced a new pair of budget-friendly wireless earbuds, but not as part of the company's affordability-focused sub-brand, CMF. The new Nothing Ear 3A are available starting today through the company's online store for $99, which is the same price as Nothing's original Ear (a). That's good news in a time when price increases are […]

Hello, everyone. When listening to a conversation, we naturally keep track of who is speaking. A program has a harder job: beyond finding speech, it must also determine where one speaker gives way to another. Today, I will use an ONNX version of Pyannote Segmentation 3.0 to detect speaker changes in a two-person conversation and split the recording into one WAV file per utterance. What I Tested This lab uses FFmpeg to decode a roughly 14-second conversation into a 16 kHz mono waveform. It then combines the Pyannote segmentation model with simple post-processing to produce contiguous speaker segments. I wanted to verify: Whether six alternating utterances can be separated into six segments Whether the detected speaker indexes remain consistent throughout the recording Whether ONNX Runtime can process the audio faster than real time using only its CPU execution provider Whether every segment can be saved as a separate WAV file The complete code and reproducible environment are available in the pyannote-scd lab in kiarina/labs . This test performs segmentation using the model's speaker indexes. It does not compare speaker embeddings or run clustering, so it is not a complete speaker diarization pipeline that identifies the same person throughout a long recording. Reproducing the Lab You will need: mise uv FFmpeg curl The following commands fetch only this lab, download the shared test audio, and run it: git clone --depth 1 --filter = blob:none --sparse \ https://github.com/kiarina/labs.git cd labs git sparse-checkout set .gitignore .mise/tasks Makefile mise.toml \ 2026/07/04/pyannote-scd make download-test-assets mise -C 2026/07/04/pyannote-scd run On the first run, the task downloads the full-precision onnx/model.onnx file from onnx-community/pyannote-segmentation-3.0 on Hugging Face. uv then prepares the Python dependencies and runs the detector. How Speaker Segments Are Detected The input is this shared test asset: assets/mp3/conversation_2speaker_14s_16k.mp3 The re

Everyone needs a safe way to listen to music on outdoor runs. We’ve found the bone conduction headphones to grab on your way out the door.

The California-based startup enters a crowded headphone market with a $399 chiseled-aluminum gamble.

With AI-powered noise reduction, an automatic speech-focusing system, and a simple, effective hearing test, it’s a shame these aids don't come with a better app.

Bose headphones are already one of our favorites for comfort, sound, and noise canceling. Now they’re cheaper.

Discover the best Bluetooth speakers of all shapes and sizes, from waterproof clip-ons to a massive boom box.

Google is betting generative AI can breathe new life into the smart speaker. The company's new $99.99 Google Home Speaker replaces the rigid commands of the Google Assistant era with more conversational Gemini interactions.

QTRACTOR BIBLE Volume 1 — Foundations PART I — QTRACTOR CONCEPTS & WORKFLOW MODEL Chapter 1 — What Qtractor Is Quick Start Qtractor is a non-destructive, multi-track audio and MIDI sequencer designed primarily for Linux-based production environments. Unlike applications that attempt to integrate every aspect of the audio ecosystem into a single package, Qtractor focuses on recording, sequencing, editing, routing, mixing, and rendering while cooperating with external audio infrastructure and specialized tools. Qtractor is best understood as a timeline-centered production environment where audio clips, MIDI clips, plugins, automation, and routing configurations are organized into sessions. Common uses include: Music production MIDI composition Podcast production Voice recording Sound design Film scoring Hybrid hardware/software studios Live backing-track preparation Design Philosophy Qtractor follows several fundamental principles: Non-Destructive Editing Source media files remain unchanged. When a clip is trimmed, split, faded, moved, stretched, or processed, Qtractor modifies references and parameters rather than rewriting the original recording. Benefits include: Unlimited experimentation Reversible editing Reduced storage requirements Safer project management Session-Based Workflow Every operation belongs to a session. A session contains: Track definitions Clip placements Bus configurations Plugin assignments Automation data Routing information Tempo maps Markers Audio and MIDI source files remain separate from session instructions. Timeline-Centered Production The timeline is the primary workspace. Nearly every task ultimately relates to a position on the timeline: Recording Editing Automation Arrangement Export Qtractor is optimized for linear productions rather than clip-launching performance systems. Open Ecosystem Integration Qtractor assumes cooperation with: Audio servers MIDI systems External synthesizers External samplers Video playback tools Modular audi

Now that we're clear of WWDC and all of the new AI-powered features coming to Apple's platforms, Bloomberg reporter Mark Gurman has more details about rumored new hardware, like the camera-equipped AirPods he'd previously written about. He says they are currently on schedule for a late 2027 launch, and that while we're checking out beta […]

Amazfit’s trail running smartwatch delivers solid off-road performance at a bargain price compared to its competitors.

Don’t be fooled by the compact size of this soundbar. It’s a solid option for smaller TVs or spaces without having to sacrifice sound quality.

Marshall has announced a new version of its stylish Stockwell Bluetooth speaker, putting a new focus on repairability. The Stockwell III, the brand's first update to this model since early 2019, features a similar design to its predecessor with a large carrying handle and speakers that pump sound in all directions, not just out the […]

🏆 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

Shokz has announced two new versions of its open earbuds. Like the original OpenDots One that launched in May 2025, the new Shokz OpenDots 2 and OpenDots Air are both designed to be worn clipped to the back of your ear with their drivers positioned to project sound toward your ear canals without blocking them. […]

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

A new reading subscription from Everand offers access to both ebooks and audiobooks, and Fable's book club community.

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

With true plug-and-play support, versatile connectivity, and excellent sound, Universal Audio’s Volt 876 makes recording simple.