🎧🚫 Reggaeton Be Gone

Edge Machine Learning device that recognizes reggaeton and triggers a Bluetooth test routine on a configured speaker

Roni Bandini — Buenos Aires, Argentina — February 2024

Reggaeton Be Gone is an experimental Raspberry Pi device inspired by TV-B-Gone.

It continuously samples ambient audio, uses an Edge Impulse audio-classification model to determine whether reggaeton is playing, displays the inference confidence on a 128×32 OLED, and—when the configured threshold is exceeded—triggers a Bluetooth test routine against a configured speaker.

The original motivation was simple: a neighboring Bluetooth speaker playing loud reggaeton every morning.

The resulting project combines:

• 🎙️ Audio acquisition
• 🧠 Edge Machine Learning
• 🎵 Music-genre classification
• 🍓 Raspberry Pi
• 📡 Bluetooth experimentation
• 🖥️ OLED feedback
• 📝 Event logging

🧷 Simpler Alternative: Pocket Gone

A later project explores the Bluetooth side without Machine Learning or a Raspberry Pi.

Pocket Gone is smaller, cheaper and portable:

👉 pocketgone.com

The evolution from Reggaeton Be Gone to Pocket Gone was also presented at Nerdearla 2025:

👉 Pocket Gone — Nerdearla / NERDflix

🏗️ Architecture

flowchart LR
    MUSIC["🎵 Ambient Music"]
    MIC["🎙️ USB Microphone"]
    PI["🍓 Raspberry Pi 3"]
    EI["🧠 Edge Impulse  
AudioImpulseRunner"]
    CLASS{"reggaeton?"}
    OLED["🖥️ 128×32 OLED"]
    LOG["📝 log.txt"]
    BT["📡 Bluetooth Test Routine"]
    SPEAKER["🔊 Configured Speaker"]

    MUSIC --> MIC
    MIC --> PI
    PI --> EI
    EI --> CLASS

    CLASS --> OLED
    CLASS --> LOG

    CLASS -->|"score > threshold"| BT
    BT --> SPEAKER

The ML inference runs locally on the Raspberry Pi using an Edge Impulse .eim deployment.

✨ Features

• 🎵 Reggaeton / other-music classification
• 🧠 Edge Impulse Linux inference
• 🎙️ USB microphone or USB audio interface
• 🖥️ 128×32 SSD1306 OLED
• 🔘 Physical start button
• 🎯 Configurable inference threshold
• 📡 Configurable Bluetooth target
• 📝 Timestamped activity log
• 🔬 Multiple experimental Bluetooth methods
• 🧪 Manual forceFire test mode
• 🖼️ Custom OLED graphics and font
• 🧱 3D-printed front panel
• 🔄 Several later experimental versions

🧠 Machine Learning

The original model was trained because common music-genre datasets such as GTZAN did not provide a dedicated reggaeton class.

The workflow was:

flowchart LR
    SONGS["🎵 Music Samples"]
    WAV["WAV  
Mono / 16 kHz"]
    SPLIT["✂️ 4 s Windows"]
    MFE["🔬 MFE"]
    NN["🧠 Classification"]
    EIM["📦 Linux ARM .eim"]
    PI["🍓 Raspberry Pi"]
    
    SONGS --> WAV
    WAV --> SPLIT
    SPLIT --> MFE
    MFE --> NN
    NN --> EIM
    EIM --> PI

Original settings documented for Version 1:

The current source expects:

model = "reggaetonbgone-linux-armv7-v4.eim"

🤗 Public Model

A public reference model is now available on Hugging Face:

👉 ronibandini/reggaetonbegone

The model card specifies:

Audio: 16 kHz WAV / mono

Labels:
- reggaeton
- otros

The published reference model was trained with only six songs, so the model card recommends training a larger dataset for improved generalization.

This is particularly important when deploying the classifier in rooms with:

• speech
• television
• other music genres
• different speakers
• background noise
• varying microphone distance

🎙️ Edge Impulse Linux Runner

The Python application uses:

from edge_impulse_linux.audio import AudioImpulseRunner

and initializes the model with:

with AudioImpulseRunner(modelfile) as runner:
    model_info = runner.init()
    labels = model_info['model_parameters']['labels']

Audio is continuously classified through:

for res, audio in runner.classifier(
    device_id=selectedDeviceId
):

The configured USB input is:

selectedDeviceId = 1

Change this value to match the desired microphone/audio interface.

🎯 Detection Threshold

Version 1.0 currently uses:

threshold = 0.95

or:

95% confidence

The program specifically watches the:

reggaeton

class.

Below the threshold, the OLED displays:

Is reggaeton?
87.42 %

The relevant logic is:

if label == 'reggaeton' and score <= threshold:
    updateScreen(
        "Is reggaeton?",
        str(round(score * 100, 2)) + " %"
    )

When the threshold is exceeded:

if label == 'reggaeton' and (
    score > threshold or forceFire == 1
):

the configured Bluetooth test action is triggered.

⚙️ Version 1.0 Configuration

The principal settings are grouped near the beginning of:

👉 reggaetonBeGone.py

myPath = "/home/pi/reggaeton/"

selectedDeviceId = 1

method = 1

targetAddr = ":::::"

packagesSize = 800
threadsCount = 1000

threshold = 0.95
myDelay = 0.1

forceFire = 0

model = "reggaetonbgone-linux-armv7-v4.eim"

The source code is intended for controlled experimentation with hardware you own.

🔘 Physical Start Button

The application does not immediately begin audio classification.

GPIO configuration:

GPIO.setmode(GPIO.BCM)

buttonPin = 26

GPIO.setup(
    buttonPin,
    GPIO.IN,
    pull_up_down=GPIO.PUD_UP
)

The program waits at:

Waiting for button...

until GPIO 26 is pulled LOW.

After the button is pressed:

Listening...

and audio inference begins.

🖥️ OLED Interface

The build uses a monochrome SSD1306 128×32 OLED.

disp = Adafruit_SSD1306.SSD1306_128_32(
    rst=None
)

The screen displays information such as:

Reggaeton BeGone
Listening...

and:

Reggaeton BeGone
Is reggaeton?
92.61 %

The UI is rendered using Pillow:

from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont

Custom font:

font = ImageFont.truetype(
    'whitrabt.ttf',
    12
)

🔌 Wiring

OLED

Enable I²C with:

sudo raspi-config

and verify the display with:

i2cdetect -y 1

Push Button

The internal pull-up is enabled in software.

Audio

Connect either:

USB microphone

or:

USB audio interface + microphone

The original build used a Behringer Xenyx 302USB.

🛠️ Hardware

An optional external Bluetooth adapter can also be used for experimental versions.

🔄 Runtime Flow

flowchart TD
    START["Power On"]
    UI["Display Target + Method"]
    WAIT["🔘 Wait for Button"]
    LOAD["🧠 Load .eim Model"]
    LISTEN["🎙️ Capture Audio"]
    CLASSIFY["Run Classification"]
    SCORE{"reggaeton > 95%?"}
    OLED["🖥️ Display Score"]
    LOG["📝 Write Log"]
    ACTION["📡 Trigger Test Routine"]

    START --> UI
    UI --> WAIT
    WAIT --> LOAD
    LOAD --> LISTEN
    LISTEN --> CLASSIFY
    CLASSIFY --> SCORE

    SCORE -->|"No"| OLED
    OLED --> LISTEN

    SCORE -->|"Yes"| LOG
    LOG --> ACTION
    ACTION --> LISTEN

📝 Logging

Every major operation is written to:

log.txt

with a timestamp:

now = datetime.datetime.now()

dtFormatted = now.strftime(
    "%Y-%m-%d %H:%M:%S"
)

Events include:

Started
Listening
AI model ...
Firing threshold ...
Interrupted

This is useful for comparing inference behavior with the audio being played during experiments.

📂 Runtime Layout

The code expects a slightly different runtime layout from the flat GitHub repository.

A practical deployment is:

/home/pi/reggaeton/
│
├── reggaetonBeGone.py
├── reggaetonbgone-linux-armv7-v4.eim
├── whitrabt.ttf
├── log.txt
│
└── images/
    └── logo.png

This matters because the current source loads:

myPath + 'images/logo.png'

while the GitHub repository currently stores logo.png at repository root.

Either create the images/ directory as above or change the image path in the Python source.

💻 Software Setup

Install Raspberry Pi OS:

👉 Raspberry Pi Imager

Enable:

SSH
I²C

through:

sudo raspi-config

The application requires:

• Python 3
• Edge Impulse Linux SDK
• PortAudio / PyAudio
• Raspberry Pi GPIO
• Pillow
• Adafruit SSD1306
• Adafruit GPIO
• BlueZ

The original complete dependency procedure is preserved in:

👉 Reggaeton Be Gone — Hackster.io

Edge Impulse Linux SDK:

👉 edgeimpulse/linux-sdk-python

🚀 Installation

Clone the repository:

git clone \
https://github.com/ronibandini/reggaetonBeGone.git

cd reggaetonBeGone

Repository:

👉 github.com/ronibandini/reggaetonBeGone

Prepare the runtime assets:

logo.png
whitrabt.ttf
Edge Impulse .eim model

Then configure:

myPath
selectedDeviceId
threshold
targetAddr
method

in:

👉 reggaetonBeGone.py

Run the application in a controlled test environment with your own Bluetooth speaker.

📁 Repository Structure

reggaetonBeGone/
│
├── reggaetonBeGone.py
├── README.md
├── LICENSE
│
├── logo.png
├── sticker.png
├── whitrabt.ttf
└── log.txt

• 🐍 reggaetonBeGone.py — Version 1.0 application
• 📝 log.txt — sample runtime log
• 🖼️ logo.png — OLED graphic
• 🏷️ sticker.png — enclosure artwork
• 🔤 whitrabt.ttf — display font
• ⚖️ LICENSE — MIT License

🧪 Versions

Version 1.0 — February 2024

The version available in this GitHub repository.

Features:

Raspberry Pi 3
Edge Impulse audio classification
128×32 OLED
GPIO start button
Configured Bluetooth target
Two experimental test methods
Activity log

Source:

👉 Version 1.0 — GitHub

Version 2.0 — April 2024

Released to participants of the Nerdearla Chile 2024 workshop.

Improvements documented by the author include:

64-bit support
On-device Bluetooth scanning
Strike system to reduce false positives
Process cleanup
Updated ML model

Workshop:

👉 Reggaeton Be Gone — Nerdearla / NERDflix

Background:

👉 Workshop Reggaeton Be Gone en Nerdearla Chile — Medium

Version 3.0 — November 2024

Created for the Ekoparty 2024 workshop.

The experimental release added further scanning, model and external-radio improvements.

Workshop announcement:

👉 Cómo armar un Reggaeton Be Gone en Ekoparty

More version history:

👉 Reggaeton Be Gone — Medium

🎥 Videos & Talks

Nerdearla Chile 2024

Complete Spanish workshop covering:

• hardware
• Machine Learning
• assembly
• limitations
• Version 2.0

▶️ Reggaeton Be Gone — NERDflix

YouTube Workshop

▶️ Reggaeton Be Gone — Spanish Workshop

Nerdearla Argentina 2025

The later talk follows the evolution from Reggaeton Be Gone to Pocket Gone.

▶️ Pocket Gone y la aventura de silenciar parlantes

🌐 External References

🛠️ Hackster.io — Full Tutorial

The original February 26, 2024 build documents the circuit, Raspberry Pi configuration, ML training, audio input, OLED interface and enclosure.

👉 Reggaeton Be Gone — Hackster.io

🇦🇷 Medium — Original Article

Detailed development history, versions, ML workflow, results, conferences and press coverage.

👉 Reggaeton Be Gone — Roni Bandini / Medium

🤗 Public Model

16 kHz mono Edge Impulse model with reggaeton and otros labels.

👉 Reggaeton Be Gone Model — Hugging Face

📰 Press

WIRED

Published February 27, 2024.

👉 Reggaeton Be Gone, la máquina impulsada por IA que bloquea bocinas Bluetooth — WIRED

Hackaday

Published February 23, 2024.

👉 Reggaeton-Be-Gone Disconnects Obnoxious Bluetooth Speakers — Hackaday

Hackster News

👉 Pardon the Interruption — Hackster

Tom's Hardware

Published February 28, 2024.

👉 La musica del vicino è fastidiosa? Ecco la soluzione da hacker — Tom's Hardware

Euronews

Published April 9, 2024.

👉 Reggaeton Be Gone: esta máquina casera silencia la música de los vecinos usando IA — Euronews

Univision

👉 Latino crea aparato para hackear y apagar bocinas que reproduzcan reggaetón — Univision

La Nación

Feature on maker culture, Reggaeton Be Gone and other experimental machines.

👉 Roni Bandini, el creador de la antena anti reggaeton — La Nación

🔗 Related GitHub Projects

🎙️ Photon 2 Voice Command

Audio keyword recognition with Edge Impulse running directly on a Particle Photon 2.

👉 github.com/ronibandini/Photon2VoiceCommand

📚 Reading Time

Audio TinyML on Arduino Nano 33 BLE Sense for recognizing the sound of paper page turns.

👉 github.com/ronibandini/ReadingTime

🚦 TI AM62A AI Traffic Light

Computer Vision inference connected to a physical control system.

👉 github.com/ronibandini/TIAM62AITrafficLight

🔊 Domestic ML LRAD

Audio output triggered by Edge Impulse Computer Vision classification.

👉 github.com/ronibandini/domesticLMLRAD

📕 Contracultura Maker

Contracultura Maker is a book by Roni Bandini about maker culture, experimental electronics, AI, physical computing and technological autonomy.

Reggaeton Be Gone is one of the projects connected with this approach to building deliberately unusual technological artifacts.

📂 Contracultura Maker — GitHub repository

📕 Download Contracultura Maker PDF

📬 Contact

Roni Bandini Maker · AI Developer · Writer Buenos Aires, Argentina

• 🐙 GitHub — @ronibandini
• 🌐 Medium — @ronibandini
• 𝕏 X / Twitter — @RoniBandini
• 📸 Instagram — @ronibandini
• ▶️ YouTube — @RoniBandini
• 💼 LinkedIn — Roni Bandini

Built with 🎵 + Raspberry Pi + Edge Machine Learning + Bluetooth.