# Querying the microphone

This section will use the audio function. SA Engine will access your microphone via the web browser when running the examples.

Java is required if you wish to test this on an SA Engine on your PC.

audio(Number ws, Number sr) -> Stream of Vector of Number

audio() returns a raw data stream of microphone readings with sampling rate sr packed into tumbling windows (i.e. vectors) of size ws.

In this tutorial it will be shown how to analyze such audio streams.

Note

This tutorial will use the microphone in your browser. The first time you run a query the browser will ask you for access to the microphone before you'll see any data.

An alternative is to define a synthetic audio signal generator along the guidelines in the tutorial Simulated audio.

Test that you have your microphone working by visualizing with Bar plot the following CQ while making some sound:

//plot: Bar plotaudio(256,16000)

Now, let's make a CQ that analyzes the audio stream in real time to detect whether it contains sound with frequencies in a given interval. We will do the following:

1. Transform the audio stream into a stream of frequency spectra windows by applying the Fast Fourier Transform rfft() on each window from the raw audio() function.

2. Find the dominating frequency in each transformed window, i.e. the index of the strongest frequency in each spectrum.

3. Select only those windows where the dominating frequency is within the given frequency interval.

The following CQ detects when the dominating frequency from the microphone is in the interval [80,400], run the query while humming:

Note

Stop the previous query to the microphone before you run this query; otherwise the microphone line will be busy.

//plot: Line plotselect Stream of hz  from Number hz, Number index, Number max, Number windowSize,       Number sampleRate, Vector of Number window where windowSize = 512   and sampleRate = 16000   and window in rfft(audio(windowSize,sampleRate))   and max = max(window)   and window[index] = max   and hz = index * sampleRate/windowSize  * 0.5   and hz >= 80   and hz <= 400
Note

The equality operator (=) specifies a filter where the expressions on the left and the right hand sides must be equal. Notice that this is a comparison, not an assignment!

You will notice that stream elements are produced even if there is silence, which happens because there is always some background noise. To filter out background noise weaker than 0.001 you can refine the CQ to:

//plot: Line plotselect Stream of hz  from Number hz, Number index, Number max, Number windowSize,       Number sampleRate, Vector of Number window where windowSize = 512   and sampleRate = 16000   and window in rfft(audio(windowSize,sampleRate))   and max = max(window)   and window[index] = max   and hz = index * sampleRate/windowSize  * 0.5   and hz >= 80   and hz <= 400   and max > 0.001

Now no stream is produced when there is silence, unless there is some background noise stronger than 0.001. Try the CQ while whistling or humming, and you will notice that the whistling does not produce any stream elements since it is outside the frequency band [80,400].

Exercise

Make a query to find out your current background noise level.

## Defining a Model Function​

In order to re-use the above CQ for different frequency bands we can define a function audio_band() representing a model that returns a stream of microphone readings for a given frequency band:

create function audio_band(Number low, Number up)                         -> Stream of Number    as select Stream of hz         from Number hz, Number index, Number max, Number windowSize,              Number sampleRate, Vector of Number w        where windowSize = 512          and sampleRate = 16000          and w in rfft(audio(windowSize,sampleRate))          and max = max(w)          and w[index] = max          and hz = index * sampleRate/windowSize  * 0.5          and hz >= low          and hz <= up          and max > 0.001

Try the model function by running the following CQ while humming or whistling:

//plot: Line plotaudio_band(80,400)

The next part of the tutorial shows how to make a model to detect unusual sounds.