# walkie-talkie Published articles for walkie-talkie. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A Walkie Talkie Interface For Claude DevFeed: [A Walkie Talkie Interface For Claude]() Original publisher: [Read original article]() Author: Zoe Skyforest Published: 2026-10-24T08:00:14Z Content type: article Language: en Sources: [Hackaday]() Topics: [Claude Code](), [Claude](), [Chat Bot]() Tags: [ai-assistant](), [artificial-intelligence](), [bluetooth](), [channel](), [claude](), [claude-code](), [delivery](), [driver](), [earbuds](), [interface](), [network](), [pc](), [projects](), [recognition](), [smartphone](), [speech](), [speech-recognition](), [text-to-speech](), [voice](), [vpn](), [walkie-talkie]() ### AI overview Walkie Claudie lets a delivery driver use Claude Code by voice while away from the computer. A smartphone connects to the PC running Claude directly, or through a VPN when remote. Speech recognition sends spoken prompts, and text-to-speech reads Claude's replies aloud. Push-to-talk can be triggered from Bluetooth earbuds, with a mode that keeps the channel open through long pauses. The excerpt also describes a retro walkie-talkie display and synthesized radio sounds. ### Source excerpt [Bookmari] had a problem -- working as a delivery driver, they wanted to work on various projects with Claude Code while out and about. Unfortunately, it simply wasn't practical to ...read more ## Descrambling split-band voice inversion with deinvert DevFeed: [Descrambling split-band voice inversion with deinvert]() Original publisher: [Read original article]() Author: Oona Räisänen (noreply@blogger.com) Published: 2017-09-12T20:31:00Z Content type: tutorial Language: en Sources: [Oona Räisänen]() Topics: [Software](), [Algorithms](), [Tool]() Tags: [algorithm](), [communications](), [radio](), [security](), [software](), [tool](), [walkie-talkie]() ### AI overview This article explains how the deinvert software tool reverses simple voice inversion and descrambles split-band inversion. It covers the filtering and mixing stages, the role of inversion carrier and split-point frequencies, and why split-band audio requires two inversion passes. ### Source excerpt Voice inversion is a primitive method of rendering speech unintelligible to prevent eavesdropping of radio or telephone calls. I wrote about some simple ways to reverse it in a previous post. I've since written a software tool, deinvert (on Codeberg), that does all this for us. It can also descramble a slightly more advanced scrambling method called split-band inversion. Let's see how that happens behind the scenes. Simple voice inversion Voice inversion works by inverting the audio spectrum at a set maximum frequency called the inversion carrier. Frequencies near this carrier will thus become frequencies near zero Hz, and vice versa. The resulting audio is unintelligible, though familiar sentences can easily be recognized. (HTML5 audio: Inverted speech.) Deinvert comes with 8 preset carrier frequencies that can be activated with the -p option. These correspond to a list of carrier frequencies I found in an actual scrambler's manual, dubbed "the most commonly used inversion carriers". The algorithm behind deinvert can be divided into three phases: 1) pre-filtering, 2) mixing, and 3) post-filtering. Mixing means multiplying the signal by an oscillation at the selected carrier frequency. This produces two sidebands, or mirrored copies of the signal, with the lower one frequency-inverted. Pre-filtering is necessary to prevent this lower sideband from aliasing when its highest components would go below zero Hertz. Post-filtering removes the upper sideband, leaving just the inverted audio. Both filters can be realized as low-pass FIR filters. This operation is its own inverse, like ROT13; by applying the same inversion again we get intelligible speech back. Indeed, deinvert can also be used as a scrambler by just running unscrambled audio through it. The same inversion carrier should be used in both directions. Split-band inversion The split-band scrambling method adds another carrier frequency that I call the split point. It divides the spectrum into two parts that are ## CTCSS fingerprinting: a method for transmitter identification DevFeed: [CTCSS fingerprinting: a method for transmitter identification]() Original publisher: [Read original article]() Author: Oona Räisänen (noreply@blogger.com) Published: 2016-10-07T13:17:00Z Content type: article Language: en Sources: [Oona Räisänen]() Topics: [fourier analysis](), [fourier transform]() Tags: [audio](), [communications](), [dsp](), [fft](), [library](), [measurements](), [radio](), [recording](), [rms](), [security](), [side-channels](), [walkie-talkie]() ### AI overview The author tests whether the 88.5 Hz CTCSS tone in handheld FM radio transmissions can help distinguish transmitters by small differences in crystal frequency and modulation depth. The recording contains thousands of short, variably noisy transmissions, captured as FM-demodulated audio and filtered below 4.5 kHz; silent passages are squelched to zero. The author uses liquid-dsp to split the recording into transmissions, bandpass-filter the CTCSS tone, and track it with a phase-locked loop (PLL). Once frequency variation falls below a threshold, the PLL is treated as locked and the tone frequency is averaged over that interval. FFT with peak interpolation is mentioned as an alternative. The excerpt also identifies limits of other candidate features: squelch can remove carrier startup, voice identification needs longer samples and sophisticated algorithms, and audio power varies with speech or noise. It does not provide the fingerprinting results. ### Source excerpt Identifying unknown radio transmitters by their signals is called radio fingerprinting. It is usually based on rise-time signatures, i.e. characteristic differences in how the transmitter frequency fluctuates at carrier power-up. Here, instead, I investigate the fingerprintability of another feature in hand-held FM transceivers, known as CTCSS or Continuous Tone-Coded Squelch System. Motivation & data I came across a long, losslessly compressed recording of some walkie-talkie chatter and wanted to know more about it, things like the number of participants and who's talking with who. I started writing a transcript - a fun pastime - but some voices sounded so similar I wondered if there was a way to tell them apart automatically. The file comprises several thousand short transmissions as FM demodulated audio lowpass filtered at 4500 Hz. Signal quality is variable; most transmissions are crisp and clear but some are buried under noise. Passages with no signal are squelched to zero. I considered several potentially fingerprintable features, many of them unrealistic: Carrier power-up; but many transmissions were missing the very beginning because of squelch Voice identification; but it would probably require pretty sophisticated algorithms (too difficult!) and longer samples Mean audio power; but it's not consistent enough, as it depends on text, tone of voice, etc. Maximum audio power; but it's too sensitive to peaks in FM noise I then noticed all transmissions had a very low tone at 88.5 Hz. It turned out to be CTCSS, an inaudible signal that enables handsets to silence unwanted transmissions on the same channel. This gave me an idea inspired by mains frequency analysis: Could this tone be measured to reveal minute differences in crystal frequencies and modulation depths? Also, knowing that these were recorded using a cheap DVB-T USB stick - would it have a stable enough oscillator to produce consistent measurements? Measurements I used the liquid-dsp library for signa