Using the sound sensor in CircuitPython is a bit more involved, because the sensor provides the actual sound data so we have to do math to turn it into loudness.

We're going to use CircuitPython, Mu and the sound sensor to plot sound levels. We'll run this code on Circuit Playground Express and use Mu to plot the data in the Serial window with Mu.

Save the following as code.py on your Circuit Playground Express board, using the Mu editor:

# SPDX-FileCopyrightText: 2018 Kattni Rembor for Adafruit Industries
#
# SPDX-License-Identifier: MIT

import array
import math
import time

import audiobusio
import board


def mean(values):
    return sum(values) / len(values)


def normalized_rms(values):
    minbuf = int(mean(values))
    sum_of_samples = sum(
        float(sample - minbuf) * (sample - minbuf)
        for sample in values
    )

    return math.sqrt(sum_of_samples / len(values))


mic = audiobusio.PDMIn(
    board.MICROPHONE_CLOCK,
    board.MICROPHONE_DATA,
    sample_rate=16000,
    bit_depth=16
)
samples = array.array('H', [0] * 160)
mic.record(samples, len(samples))

while True:
    mic.record(samples, len(samples))
    magnitude = normalized_rms(samples)
    print(((magnitude),))
    time.sleep(0.1)

# SPDX-FileCopyrightText: 2018 Kattni Rembor for Adafruit Industries
#
# SPDX-License-Identifier: MIT

import array
import math
import time

import audiobusio
import board


def mean(values):
    return sum(values) / len(values)


def normalized_rms(values):
    minbuf = int(mean(values))
    sum_of_samples = sum(
        float(sample - minbuf) * (sample - minbuf)
        for sample in values
    )

    return math.sqrt(sum_of_samples / len(values))


mic = audiobusio.PDMIn(
    board.MICROPHONE_CLOCK,
    board.MICROPHONE_DATA,
    sample_rate=16000,
    bit_depth=16
)
samples = array.array('H', [0] * 160)
mic.record(samples, len(samples))

while True:
    mic.record(samples, len(samples))
    magnitude = normalized_rms(samples)
    print(((magnitude),))
    time.sleep(0.1)

This code imports the audiobusio, time, board, array and math libraries. There are also two helper functions. The first one uses math to return a mean, or average. It is used in the second helper. The second felper function uses math to return a normalised rms average. These functions are then used to take multiple sound samples really quickly and average them to get a more accurate reading.

Next the microphone object and the samples variable are set. Then initialization of the mic object so it's ready for use.

The mic object starts taking sound samples. The normalised rms is used to find the average of a given set of samples, and that is the magnitude. Last, print the magnitude to the serial console.

Press the Serial button to see the values being printed out. Press the Plotter icon to have Mu plot those values also. Note that the Mu plotter looks for tuple values to print. Tuples in Python come in parentheses () with comma separators. If you have two values, a tuple would look like (1.0, 3.14) Since we have only one value, we need to have it print out like (1.0,) note the parentheses around the number, and the comma after the number. Thus the extra parentheses and comma in print(((magnitude),)).

Once you have everything setup and running, try speaking towards the Circuit Playground Express, and watch the plotter immediately react! Move further away from the board to cause smaller changes in the plotter line. Move closer to the board to see bigger spikes!

It's a really easy way to test your microphone and see how it reads sound changes on the Circuit Playground Express!

Advanced - an Analog Sound Meter

Some Makers look to build a classic sound meter - the rainbow meter that measures sound with sample normalization and logarithmic scaling. 

If this is the type of code you're looking to implement, see this guide for the CircuitPython code.