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You can build a wearable prototype that tracks changes in surrounding sound with Seeed Studio’s XIAO ESP32S3 Sense. Its microphone example reads audio samples and plots changing values in Arduino IDE’s Serial Plotter; it does not produce a calibrated decibel (dB SPL) reading. The simplest version can show sound trends without saving audio.
What the XIAO ESP32S3 Sense can monitor
The board variant is essential: Seeed Studio’s Microphone Usage for Sense Version tutorial says it applies only to XIAO ESP32S3 Sense. The Sense expansion board adds the microphone and microSD facilities used by the audio examples.
Seeed’s microphone example reads the microphone over I2S/PDM using GPIO 41 for PDM data and GPIO 42 for the PDM clock. It initializes capture at 16 kHz, 16-bit mono and displays changing readings in Arduino IDE’s Serial Plotter as an indication of environmental loudness. That output is useful for observing relative changes, such as a quieter or louder environment, but it is not a calibrated sound-pressure-level measurement.
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| Build | What it does | Data retained | Complexity and privacy |
|---|---|---|---|
| Loudness trend monitor | Reads microphone samples and plots changing values. | The cited example plots readings; it does not require saved audio. | The simplest option. Avoiding audio files reduces retained sensitive data, but does not by itself establish a complete privacy guarantee. |
| Audio recorder | Saves captured audio as WAV files to microSD. | Audio is retained on removable storage. | Requires a compatible card and recording workflow. Audio may contain identifiable speech, so decide who can access recordings and when they will be deleted. |
| Keyword-spotting prototype | Collects WAV examples and trains a TinyML model using Edge Impulse. | Sample recordings are used in data collection; the training workflow is separate from simple loudness plotting. | More involved software and data preparation. Keyword spotting is not a general sound-event classifier or calibrated noise measurement. |
Set up the loudness trend monitor
- Confirm the board: use XIAO ESP32S3 Sense with its microphone-equipped expansion board. A different XIAO ESP32S3 variant is not the model covered by Seeed’s microphone tutorial.
- Install and check the Arduino environment: load Seeed’s microphone example and check its compatibility with the Arduino-ESP32 version installed on your computer. Seeed notes that framework versions can have API differences, so an example may need adjustment if the APIs do not match.
- Capture microphone samples: the example uses PDM input on GPIO 41 (data) and GPIO 42 (clock), configured for 16 kHz, 16-bit mono.
- Open the Serial Plotter: run the sketch and view its changing readings in Arduino IDE’s Serial Plotter. Treat the plot as a relative sound trend, not as a dB SPL value.
Record audio to microSD only if you need it
For saved audio, Seeed’s microphone tutorial describes recording WAV audio to microSD. It specifies cards up to 32 GB, formatted as FAT32. A card is not needed for the basic plotted loudness example; add one only if the project needs recordings.
#1 Best Overall
- Powerful MCU Board: Incorporate the ESP32 S3 32-bit, dual-core, Xtensa processor chip operating up to 240 MHz, mounted multiple development ports, Arduino / MicroPython supported
- Advanced Functionality: Detachable OV2640 camera sensor for 1600*1200 resolution, compatible with OV3660 camera sensor, integrating additional digital microphone
- Great Memory for more Possibilities: Offer 8MB PSRAM and 8MB FLASH, supporting SD card slot for external 32GB FAT memory
- Outstanding RF performance: Support 2.4GHz Wi-Fi and BLE dual wireless communication, support 100m+ remote communication when connected with U.FL antenna
- Thumb-sized Compact Design: 21 x 17.5mm, adopting the classic form factor of XIAO, suitable for space-limited projects like wearable devices
Recording changes the project’s privacy profile: a WAV file can preserve speech and other identifiable sounds. Set an access and deletion policy before recording, and do not assume that using removable storage makes a recording private.
Keyword spotting is a separate workflow
Seeed’s keyword-spotting tutorial describes preparing the XIAO ESP32S3 Sense, a microSD card, card reader, and USB-C data cable, then collecting WAV samples and training a model with Edge Impulse. Its workflow requires PSRAM to be enabled. Check the tutorial alongside the installed Arduino-ESP32 version because framework API differences can affect examples.
Rank #2
- Powerful ESP32-S3 Dual-Core Processor with Built-in NPU for Onboard AI：Equipped with ESP32S3 32-bit dual-core LX7 MCU running up to 240MHz, built-in 512KB SRAM plus dedicated NPU neural accelerator supporting INT8/FP16 AI inference for pose detection & image classification. esp32 cam Hardware floating-point acceleration and independent RTC peripheral coprocessor cut main CPU load drastically, enabling stable local AI vision calculation without extra external chips
- Oversized Upgraded Memory esp32 camera for Large Program & High-Res Image Storage：Comes pre-soldered with 16MB SPI NOR Flash and 8MB PSRAM, ample cache for high-definition camera frame buffering, multi-task operation and OTA remote firmware upgrade. Reserved SPI slot for expandable max 128GB SD card to store massive captured video/data; hardware firmware encryption & secure boot prevents program tampering and reverse engineering effectively
- Dual-Band Wi-Fi + BLE5.0 Mesh for Long-Range Stable Wireless Connection：esp32 cam with antenna Features 2.4GHz 802.11b/g/n Wi-Fi up to 150Mbps with WPA3 secure encryption, supporting Station/AP hybrid working mode. Integrated Bluetooth 5.0 with BLE low power & classic Bluetooth, Bluetooth Mesh links over 200 terminal nodes; long-distance BLE transmission reaches over 1000m in open space, ideal for multi-device IoT linkage & remote camera wireless preview
- Rich Multifunctional Peripheral Ports & Onboard Multi Sensors for DIY Expansion：32 reusable interrupt-enabled GPIO pins, including 20CH 12-bit ADC, 3×SPI, 2×I2C,3×UART,2×I2S audio port,2×DAC & 8CH PWM for motor/LED control. All-in-one Type-C for power, data download & firmware flashing, plus onboard 3.7V lithium battery charging circuit(max 1A charge current). Pre-installed precision temp sensor(±0.1℃,-40~125℃) and 6-axis inertial gyro/accelerometer, compatible with most I2C/SPI external sensors for smart home & robot projects
- Multi-Voltage Power Supply & Full Security + Multi Low-Power Modes：Supports 3 power options: Type-C 5V input, 3.7V Li-ion(300~2000mAh) and external 3.3V~5V DC input, built-in full protection against overcharge/over-discharge/short circuit. Four graded low-power consumption modes from 120mA active down to 1μA deep hibernation with RTC/sensor wakeup. esp32 camera module On-chip AES/SHA/RSA hardware encryption, unique UID & anti-tamper auto data erase function to secure your IoT device data
This is a distinct machine-learning project, not an automatic extension of the Serial Plotter example. It recognizes trained keywords rather than providing calibrated sound measurements or reliably classifying every environmental event.
Make the prototype wearable
Seeed’s board guide gives the XIAO family a 21 × 17.8 mm form factor and identifies the Sense variant’s camera, microphone, and SD support. The camera is not needed for audio monitoring. The guide notes that later units changed the camera component from OV2640 to OV3660; this matters when following camera examples or buying against older camera-specific instructions, not for the microphone monitor.
Rank #3
- Powerful MCU Board: Incorporate the ESP32-S3 32-bit, dual-core, Xtensa processor running at up to 240MHz, mounted multiple development ports, Arduino / MicroPython supported
- Outstanding RF performance: supports 2.4GHz WiFi and BLE 5.0 dual wireless communication, support 100m+ remote communication when connected with U.FL antenna
- Elaborate Power Design: lithium battery charge management capability, offer 4 power consumption model which allows for deep sleep mode with power consumption as low as 14μA
- Thumb-sized Compact Design: 21 x 17.5mm, adopting the classic form factor of XIAO, suitable for space limited projects like wearable devices
- Perfect for Production: Breadboard-friendly & SMD design, no components on the back
A compact board is only one part of a wearable. The project still needs appropriate power and a mounting method that keeps the board secure and the microphone exposed to the surrounding sound. Seeed’s wearable reference-design article, dated 2025-08-15, shows examples of a XIAO wearable, printed mount, and compact Li-ion battery, but does not establish runtime or comfort for this audio-monitoring build. Choose a battery and enclosure for the actual implementation and test them in use; no battery life, comfort, or enclosure durability is established for this prototype.
Quick Recap
Best Value
- Powerful MCU Board: Incorporate the ESP32S3 32-bit, dual-core, Xtensa processor running at up to 240MHz, mounted multiple development ports, Arduino / MicroPython supported
- Outstanding RF performance: Supports 2.4GHz WiFi and BLE 5.0 dual wireless communication, supports 100m+ remote communication when connected with U.FL antenna
- Elaborate Power Design: Lithium battery charge management capability, offers 4 power consumption model which allows for deep sleep mode with power consumption as low as 14μA
- Thumb-sized Compact Design: 21 x 17.5mm, adopting the classic form factor of XIAO, suitable for space limited projects like wearable devices
- Perfect for Production: Breadboard-friendly & SMD design, no components on the back
Rank #4
- Ready for Meshtastic: Start a LoRa mesh build faster with pre-flashed Meshtastic firmware. Use it to join or create a mesh network, test node behavior, or begin a DIY off-grid messaging project
- ESP32-S3 + SX1262 Wireless Core: Built around a dual-core ESP32-S3 MCU and SX1262 LoRa radio, supporting 862–930MHz LoRa plus 2.4GHz Wi-Fi and BLE 5.0 for mesh, router and sensor projects
- Low-Friction Starter Kit: The press-fit board design reduces basic assembly work, while the included antenna setup helps new makers avoid starting from a bare board with missing RF accessories
- Arduino, MicroPython and Grove Expansion: Use I2C, UART, SPI, GPIO/PWM and ADC access with compatible XIAO expansion boards or Grove modules to add sensors, displays or custom functions
- Compact Platform, Flexible Builds: The 21 × 18 mm XIAO form factor fits compact prototypes, wearables and embedded devices, while modular add-ons let you choose the GPS, display, power and enclosure your project needs
What this prototype cannot establish
- Calibrated noise level: the cited example reports changing sample readings, not dB SPL. The available documentation does not provide a calibration method, accuracy figure, frequency response, or weighting specification.
- Safety or compliance: do not use this build as evidence for occupational noise exposure, hearing safety, medical decisions, or legal claims. Those uses require validated measurements that these examples do not establish.
- Continuous recording behavior: the recording example writes WAV audio to removable storage, but the cited documentation does not define a privacy policy for a finished wearable. Recording should be an explicit choice, with access and deletion handled by the builder.
- Wireless or cloud behavior: behavior depends on the particular implementation; the cited audio examples do not establish how a custom project handles network transmission.
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