Difference between revisions of "EspAudioSensor"

From RevSpace
Jump to navigation Jump to search
m (Ideas)
Line 25: Line 25:
** See [https://www.bluetooth.com/specifications/specs/gatt-specification-supplement-5/ GATT_Specification_Supplement_v5] paragraph 3.151, it has a noise characteristic with 1 dB resolution.
** See [https://www.bluetooth.com/specifications/specs/gatt-specification-supplement-5/ GATT_Specification_Supplement_v5] paragraph 3.151, it has a noise characteristic with 1 dB resolution.
** 0x27C3 is the GATT unit for sound pressure, see https://btprodspecificationrefs.blob.core.windows.net/assigned-values/16-bit%20UUID%20Numbers%20Document.pdf
** 0x27C3 is the GATT unit for sound pressure, see https://btprodspecificationrefs.blob.core.windows.net/assigned-values/16-bit%20UUID%20Numbers%20Document.pdf
** 0x181A is the GATT environmental sensing service
** 0x181A is the GATT environmental sensing service, document name "ESP_V1.0.0.pdf"
** 0x2A6E/0x2A6F is temperature/humidity, specified in https://btprodspecificationrefs.blob.core.windows.net/assigned-values/16-bit%20UUID%20Numbers%20Document.pdf
** 0x2A6E/0x2A6F is temperature/humidity, specified in https://btprodspecificationrefs.blob.core.windows.net/assigned-values/16-bit%20UUID%20Numbers%20Document.pdf
** See https://programmaticponderings.com/2020/08/04/getting-started-with-bluetooth-low-energy-ble-and-generic-attribute-profile-gatt-specification-for-iot/
** See https://programmaticponderings.com/2020/08/04/getting-started-with-bluetooth-low-energy-ble-and-generic-attribute-profile-gatt-specification-for-iot/

Revision as of 22:52, 26 November 2021

Project ESP audio sensor
ESP32-based audio sensor
Status In progress
Contact bertrik
Last Update 2021-11-26


This project is about creating an audio level meter, for example as an environmental noise measuring node in a citizen science project or as a standalone "decibel meter".

Marcel Meek built a sound sensor based on an ESP32 board with built-in LoRa-chip. It samples audio from a digital I2S microphone, analyses it into sub-bands, then forwards it over LoRaWAN. His code can be found here: https://github.com/meekm/LoRaSoundkit

From TheThingsNetwork, the data is picked up by a kind of 'integration' that sends it to sensor.community. This is handled by my noise forwarder Java project.

Links back:


Measuring audio as a citizen science project

The concept is the following:

  • A small box containing a microphone outside your house measures environmental sounds (traffic, etc), for example it takes a 1 second audio recording every 10 seconds
  • A spectral analysis is made of the audio (separating it in different frequency bands), calculating sound intensity for each individual band
  • Every 5 minutes, the measured sound intensity is sent to a central server on the internet. Because we only communicate intensities, this does not reveal audio fragments (conversations for example).
  • The central server takes the measured intensities and can do corrections, like a microphone specific correction, or a correction to apply A-weighting
  • The central server forwards the measurements to sensor.community, from there they are automatically picked up by RIVM

External things to investigate:


The audio spectrum is divided up into octaves and the total energy in each octave is calculated. We can then easily apply sensor/housing specific corrections, do A weighting, etc.

A weighting

Subjective audio levels are generally calculated on a logarithmic scale in dB using "A-weighting". A-weighting calculates a subjective loudness level from a physical loudness, applying a correction factor for each (part of an) octave band.

Octave start Octave center Octave end Remark
- - 22627 Hz sample rate
5657 Hz 8000 Hz 11314 Hz
2828 Hz 4000 Hz 5657 Hz
1414 Hz 2000 Hz 2828 Hz
707 Hz 1000 Hz 1414 Hz
354 Hz 500 Hz 707 Hz
177 Hz 250 Hz 354 Hz
88 Hz 125 Hz 177 Hz
44 Hz 63 Hz 88 Hz
22 Hz 31 Hz 44 Hz

List of A-weighting coefficients

The sampling rate is chosen such that the FFT bands align exactly with the bands as defined in the A-norm.


The energy in each octave is calculated by applying an FFT (fast fourier transform) on the audio data. The FFT takes in real values and outputs complex values. The intensity in each octave band is by summing the energy in a set of FFT 'bins'. The energy in each bin is calculated as the real part squared plus the imaginary part squared. The division of the audio spectrum for the FFT is chosen so it matches the octaves used in the A-weighting as described above. I plan to use the 'flat-top' window because it has good properties for measuring power levels.

Investigation into FFT libraries:

  • arduinoFFT does all calculations in double, severely limiting the audio buffer size. For each sample, you need two doubles, so that's 16 bytes per sample. I can use about 6000 samples in the audio buffer. Using floats gives me twice the audio buffer size.
  • Alternatively, esp32-fft looks nice, it uses floats instead of doubles and is optimised for esp32, but it uses malloc internally ... Also I can't use it as an Arduino library because it doesn't have the arduino library structure (with a src dir, examples dir, library.json file, library.properties file, etc) Maybe I can still use it by copying it in my sketch.


-> I went with ArduinoFFT, modified it to use float instead of double

Decibel meters

Commercially available meters:

Fairly typical specs:

  • dynamic range: 30 - 130 dB
  • accuracy: 1.5-2 dB
  • frequency range: 31.5 Hz - 8 kHz (!)
  • norm: EN 61672-1


The physical device consists of:

  • an ESP32, it has an I2S digital audio input for sampling data from a digital microphone and a WiFi interface to communicate things to the internet. It has more internal RAM than an ESP8266 (for example), this helps to take a larger audio sample and do analysis on it. It is still comparatively simple, cheap, easy to flash and powerful enough to do communication over WiFi and do audio analysis.
  • a digital I2S microphone, like the INMP441 (datasheet)

Waag society uses the Invensense ICS4342 microphone in their kit 2.1. Myself, I ordered a couple of INMP441 microphones from Aliexpress.


My initial prototype used an NodeMCU ESP32.

The microphone is connected to the microcontroller as follows (old):

  • INMP441 GND to ESP32 GND
  • INMP441 VDD to ESP32 3.3V
  • INMP441 SD to ESP32 A4/32
  • INMP441 SCK to ESP32 A16/14
  • INMP441 WS to ESP32 15
  • INMP441 L/R to ESP32 GND

The connection carries only digital signals (max 3 MHz or so). No sensitive analog electronics are needed, the microphone and the microcontroller are simply connected using "dupont" wire.

The I2S clock signal is 64 times higher than the sample clock, so at a sample rate of 44100 Hz, this means 2.8 MHz. This might be a bit high for a random wire, probably we should keep this connection short. At a sample rate of 22627 Hz, this results in an I2S clock of 1.45 MHz.

Heltec LoRa32 v2

Connection of an Heltec LoRa32 v2 will be described here.


  • INMP441: cheap microphone
  • SPH0645: untested
  • ICS-43434: expensive, used in the DNMS


Original experimental code

My original experimental initial code for sampling audio from the digital microphone, calculating a frequency spectrum and displaying it, can be found on github. This code is in its current form not usable as a citizen science audio meter.

What the software demonstrates:

  • Take an audio measurement from a digital microphone at a regular interval.
  • On the recorded audio, perform a 4096-point real->complex FFT with a windowing function (flat-top).
  • Calculate power for each FFT-bin (Im-squared + Re-squared) and sum up bins per octave.
  • Display as a kind of ASCII-art 'waterfall'.

Citizen science code


Marcel Meek has written an Arduino firmware that can actually be used in a citizen science sensor: https://github.com/meekm/LoRaSoundkit

backend code

I have written a forwarder application in Java that takes the data from that sensor from TheThingsNetwork and forwards it to sensor.community: https://github.com/bertrik/noiseforwarder.

Use as a citizen science sound/noise sensor

The sensor.community node has support for a "DNMS" sound sensor, see https://sensor.community/en/sensors/dnms/

We emulate the network protocol used by a sensor.community node for DNMS, sending a "LAeq", minimum and maximum value.


In my opinion, the DNMS module has the following disadvantages:

  • the module produces a single value with a minimum and a maximum value (not a spectrum), no spectral corrections are possible
  • the communication interface is I2C, where the sound sensor is I2C slave, which is more difficult to implement than (for example) a 9600 bps serial interface
  • the communication protocol is a bit weird, e.g. sends an 8-bit CRC for every 16-bit data word
  • it needs a custom PCB

It would be nice to be compatible with this sensor, so an ESP32 audio sensor could be a drop-in replacement for the "official" DNMS module.

DNMS Protocol:

  • as reverse engineered from the sensor.community firmware:
    • device is at i2c address 0x55 (7-bit or 8-bit address?)
    • commands exchanged are 16-bit
      • 1 DNMS_CMD_RESET
    • a command looks like this:
      • 16-bit (big endian) command id
      • a variable number of 16-bit data words, each with an 8-bit CRC
    • getting the data from the DNMS:
      • send the DNMS_CMD_CALCULATE_LEQ command
      • repeat DNMS_CMD_READ_DATA_READY command until it indicates that a measurement is available
      • send the DNMS_CMD_READ_LEQ command, this results in three values: a, a_min and a_max
      • if an error occurred, send the DNMS_CMD_RESET command

Conclusion: Use of the I2C slave protocol makes it very difficult to use an ESP32 to emulate a DNMS sensor.

Sensor.community protocol

The protocol is basically the same as the particulate matter concentration protocol, with different "value_type" fields in the JSON.

  • REST with JSON
  • HTTP POST request to "https://api.sensor.community" with path "/v1/push-sensor-data/"
  • the POST contains the following HTTP headers:
    • HTTP header "X-Sensor" contains the unique hardware id, for an ESP32-board, an id of type "esp32-<number>" would be appropriate
    • HTTP header "X-Pin" with value 15
    • HTTP header "Content-type" with value "application/json"
  • the POST body contains the following fields:
    • "software_version", description of the software version
    • "sensordatavalues", list of structures:
      • "value_type" = "noise_LAeq", "value" = value in dB
      • "value_type" = "noise_LA_min", "value" = value in dB
      • "value_type" = "noise_LA_max", "value" = value in dB