TGTGInsighttelegram intelligenceLIVE / telegram public index
← GitHub Trends

TGINSIGHT SIMILAR POSTS

Find similar content

Source channel @githubtrending · Post #15367 · Dec 25

#cplusplus#arduino#ble_jammer#ble_spoof#ble_spoofer#cybersecurity#deauther#esp32#hack#hacktoberfest#jammer#nrf_scanner#nrf24l01#sour_apple nRFBOX is a handheld ESP32-based tool that scans and analyzes the 2.4 GHz band (Wi‑Fi, BLE, etc.), shows signal strength and channel activity, and can run jamming, BLE jamming/spoofing, and Wi‑Fi deauthentication tests for security research and troubleshooting. It combines an ESP32, NRF24 modules, OLED display, battery management, and SD support for firmware and logging, with notes about limited range, device variability, and power limits when using multiple NRF modules. Benefit: you can use it to find crowded channels, diagnose wireless interference, and test network/device resilience in controlled, legal test environments. https://github.com/cifertech/nRFBox

Results

3 similar posts found

Search: #sounds

当前筛选 #sounds清除筛选
Interesting Planet 🌍

@interesting_planet_facts · Post #1053 · 11/19/2025, 06:11 PM

🌎 In 1977, the Soviet Venera 14 probe recorded mysterious low-frequency “thunder”-like sounds on Venus. Scientists now attribute these to seismic activity or wind interacting with the planet’s dense atmosphere. Venus’s surface winds move slowly, but thick air carries sound much farther than on Earth. ✨ #Venus⚡#sounds⚡#space 👉subscribe Interesting Planet 👉more Channels ​

djangoproject

@djangoproject · Post #255 · 02/02/2017, 06:57 PM

https://github.com/tyiannak/pyAudioAnalysis #pyAudioAnalysis is a Python library covering a wide range of audio analysis tasks. Through pyAudioAnalysis you can: Extract #audio features and representations (e.g. mfccs, spectrogram, chromagram) Classify unknown #sounds Train, parameter tune and evaluate classifiers of audio segments Detect audio events and exclude silence periods from long recordings Perform supervised segmentation (joint segmentation - classification) Perform unsupervised segmentation (e.g. speaker diarization) Extract audio thumbnails Train and use audio regression models (example application: emotion recognition) Apply dimensionality reduction to visualize audio data and content similarities