标签：#Audi

If you're building — or integrating — a browser-based audio trimmer, the question that eventually reaches your inbox isn't "does it cut audio?" The real question is: does it cut audio correctly across the inputs we actually receive from users? That shift, from feature presence to behavior under fuzzy conditions, is what turns a demo into a product. This article walks through the QA matrix I use when reviewing client-side trimmers before release, with an emphasis on the silent failures that don't show up in a happy-path recording. The tool under review for most of this article is the Lizely audio cutter ( in-depth walkthrough ), but the principles apply to any browser trimmer that decodes via AudioContext or OfflineAudioContext . What "Trim" Actually Means Once You Leave the Lab In the lab, you upload a 44.1 kHz stereo WAV, drag two handles, click export, and verify the output. In production, users upload M4A recordings from iPhone Voice Memos, AMR files from old Android handsets, mono 8 kHz captures from cheap conference mics, and — occasionally — files renamed from .wav to .mp3 without re-encoding. Each of those paths stresses a different layer of the pipeline. The first thing to test, before any UI work, is the decode step. Browsers expose this through the decodeAudioData method on BaseAudioContext , documented on MDN's BaseAudioContext page . MDN is explicit about something engineers often miss: decodeAudioData detaches the input ArrayBuffer . If your trimmer holds a reference to the original buffer for "undo" and reuses it, you'll decode an empty buffer the second time around and get a silent result. That's a real defect class, not a theoretical one. The second thing to test is what happens when decoding fails. The spec says decodeAudioData invokes the error callback with a DOMException , but the browser-specific error messages vary. Chrome tends to surface "Decoding error" with no detail; Firefox appends the underlying codec name. Your QA suite should assert on

Bose announced a second-generation version of its entry-level QuietComfort noise-canceling headphones that were originally introduced in late 2023. The new headphones feature a refreshed design with a more comfortable headband that's easier to adjust and ANC that better adapts to changing noise environments. The second-gen QuietComfort are also getting Bose's TrueSpatial immersive audio technology that […]

Open earbuds are having a bit of a moment right now, and Nothing is the latest company jumping on the trend. Its budget sub-brand has introduced the CMF Clip Pro, CMF's first open-ear buds that are designed to provide comfort and sound quality for people who don't want to sacrifice their situational awareness. The $99 […]

Whether you want an audio player that’ll grow with your kid or one that your toddler can operate independently, I have a recommendation for you.

The $100 earbuds look and sound better than earbuds nearly twice the price—as long as you can stomach just average noise canceling.

moin, ich möchte euch mein aktuelles Projekt vorstellen: Die Wiederbelebung eines Alienware m17x (Baujahr 2008) als dedizierte DJ-Workstation unter Linux. Ziel war es, alte Hardware nachhaltig zu nutzen und eine stabile Umgebung für Mixxx zu schaffen. Die Hardware: Notebook: Alienware m17x (Core 2 Duo, 4GB RAM, SSD) OS: KDE Neon mit Low-Latency-Kernel (6.8.0) Software: Mixxx 2.4 Audio-Interface: Günstiges USB-Audio-Device für den Master-Ausgang Das Problem: Mixxx verweigerte unter ALSA den Dienst mit der Fehlermeldung: Error opening "USB Audio Device (hw:1,0)" - Invalid sample rate Die Analyse über /proc/asound/card1/stream0 zeigte die Ursache: Das USB-Gerät unterstützt ausschließlich 46875 Hz – eine für Audio-Interfaces sehr unübliche Rate, die weder 44100 Hz noch 48000 Hz entspricht. Der direkte Zugriff über hw:CARD=Device,DEV=0 schlug fehl. Die Lösung: Die Rettung war die Aktivierung der ALSA-Plug-Erweiterung über PipeWire/ALSA, die eine automatische Sample-Rate-Konvertierung erlaubt. Starten Sie Mixxx nicht direkt, sondern setzen Sie zuvor die Umgebungsvariable: export PA_ALSA_PLUGHW=1 mixxx Damit Mixxx auch dauerhaft korrekt startet (z.B. über das KDE-Menü), habe ich den Starter wie folgt angepasst: bash -c "export PA_ALSA_PLUGHW=1; mixxx" Ergebnis: ✅ Master-Ausgabe über das USB-Device funktioniert stabil. ✅ Kopfhörer-Vorhören (C-Media USB Headphone Set) läuft parallel. ✅ Das System läuft trotz des Alters der Hardware (2008) flüssig und mit geringer Latenz. Die vollständige Dokumentation inklusive Fotos des Umbaus, der genauen Kernel-Einstellungen und der Konfiguration findet ihr in meinem Open-Source-Repository: 👉 [ https://github.com/qrishii/DJ-Installationen ] Ich hoffe, diese Lösung hilft anderen, die ähnliche Probleme mit exotischen USB-Audio-Raten unter Linux haben! das Leben ist lustig

Its latest Pro headset can mix between four inputs and has improved wireless connectivity over the originals.

The company’s first headphones look very stylish and have great tactile features, but underwhelm on sound quality.

The new full-size flagship SUV starts at $87,700 when it goes on sale in Q4.

Tech companies love to sell us expensive gadgets to solve all of life's little problems. Sleepbuds sold by the likes of Anker and Ozlo are a good example. These miniature marvels of engineering sit flush in the ear, and allow side-sleepers to doze off listening to podcasts, audiobooks, music, or white noise without annoying their […]

The Media Router is a tool that automatically selects the best image, video, or audio generation model for a request based on whether a developer prioritizes quality, speed or cost.

More than one-eighth of apps analyzed contained foreign code.

Skullcandy’s audio products aren’t exactly known for their stellar audio quality or noise cancellation, but its latest headphones are getting an assist from Bose to turn things around.

Skullcandy announced a new version of its Crusher wireless headphones today featuring a few of Bose's audio technologies including its QuietControl ANC and head-tracking spatial audio. The Crusher headphone line differentiates itself from the competition through the use of both full-range and dedicated bass drivers in each ear cup to boost deeper frequencies. Skullcandy admits […]

Now it's an arms race between OEMs locking down chips and tuners trying to crack them.

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.