标签：#Audi

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.

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.

Most RAG demos answer "what's the right chunk?" Very few can answer the two questions a regulator or an auditor will actually ask: Replay this decision — show me the exact, complete record of how this answer was produced. Reconstruct the past — what did your system know at the moment it answered, not what it knows now? I got tired of hand-waving at both, so I shipped two pre-registered, deterministic benchmarks alongside JAMES , my local-first, audit-native Graph-RAG. Pre-registered means the metrics, scenarios, and decision rules were locked before the numbers came in — no post-hoc story-fitting. RAB — Replayable-Audit Benchmark RAB measures whether your audit trail is good enough to replay a decision, with three deterministic metrics: Metric What it checks EU AI Act AC — Audit Completeness Is every decision-relevant event logged? Art. 10 RF — Replay Fidelity Can you re-derive the answer from the log alone? Art. 12 PC — Provenance Coverage Does every claim trace to a source? Art. 19 The three metrics map verbatim to EU AI Act Articles 10, 12, and 19 — record-keeping obligations that apply from 2026-08-02 (per Article 113). Scenario S1 result: AC RF PC JAMES 1.000 1.000 1.000 Baseline-0 0.275 0.000 0.000 (vanilla default-logging) The gap is the whole point. "We have logs" (AC 0.275) is not the same as "we can replay the decision" (RF 0). Default application logging gets you a partial event trail and zero replay/provenance — which is exactly the failure mode an Article 12 audit would surface. LRB — Lifecycle Retrieval Benchmark RAG facts go stale. A policy is superseded, a price changes, a spec is revised. LRB asks: when you query as of a point in time, do you retrieve the fact that was valid then , or whatever overwrote it? Three systems compared: V — Vanilla : no time handling. N — Naive-supersede : newest fact wins. J — JAMES : validity-window retrieval ( reconstruct_graph_at(t) ). The R@1 ordering V < N < J holds across 4 model families × 4 scale points (a 12.5×