In this tutorial, you'll use machine learning to build a system that can recognize audible events, particularly your voice through audio classification. The system you create will work similarly to "Hey Siri" or "OK, Google" and is able to recognize keywords or other audible events, even in the presence of other background noise or background chatter.

You'll learn how to collect audio data from microphones, use signal processing to extract the most important information, and train a deep neural network that can tell you whether your keyword was heard in a given clip of audio. Finally, you'll deploy the system to an embedded device and evaluate how well it works.

At the end of this tutorial, you'll have a firm understanding of how to classify audio using Edge Impulse.

There is also a video version of this tutorial:

You can view the finished project, including all data, signal processing and machine learning blocks here: Tutorial: responding to your voice.

1. Prerequisites

For this tutorial, you'll need a supported device.

If your device is connected under Devices in the studio you can proceed:

2. Collecting your first data

In this tutorial we want to build a system that recognizes keywords, so your first job is to think of a great one. It can be your name, an action, or even a growl - it's your party. Do keep in mind that some keywords are harder to distinguish from others, and especially keywords with only one syllable (like 'One') might lead to false-positives (e.g. when you say 'Gone'). This is the reason that Apple, Google and Amazon all use at least three-syllable keywords ('Hey Siri', 'OK, Google', 'Alexa'). A good one would be "Hello world".

To collect your first data, go to Data acquisition, set your keyword as the label, set your sample length to 10s., your sensor to 'microphone' and your frequency to 16KHz. Then click Start sampling and start saying your keyword over and over again (with some pause in between).

Note: Data collection from a development board might be slow, you can use your Mobile phone as a sensor to make this much faster.

Afterwards you have a file like this, clearly showing your keywords, separated by some noise.

This data is not suitable for Machine Learning yet though. You will need to cut out the parts where you say your keyword. This is important because you only want the actual keyword to be labeled as such, and not accidentally label noise, or incomplete sentences (e.g. only "Hello"). Fortunately the Edge Impulse Studio can do this for you. Click ⋮ next to your sample, and select Split sample.

If you have a short keyword, enable Shift samples to randomly shift the sample around in the window, and then click Split. You now have individual 1s. long samples in your dataset. Perfect!

3. Building your dataset

Now that you know how to collect data we can consider other data we need to collect. In addition to your keyword we'll also need audio that is not your keyword. Like background noise, the TV playing ('noise' class), and humans saying other words ('unknown' class). This is required because a machine learning model has no idea about right and wrong (unless those are your keywords), but only learns from the data you feed into it. The more varied your data is, the better your model will work.

For each of these three classes ('your keyword', 'noise', and 'unknown') you want to capture an even amount of data (balanced datasets work better) - and for a decent keyword spotting model you'll want at least 10 minutes in each class (but, the more the better).

Thus, collect 10 minutes of samples for your keyword - do this in the same manner as above. The fastest way is probably through your mobile phone, collecting 1 minute clips, then automatically splitting this data. Make sure to capture wide variations of the keyword: leverage your family and your colleagues to help you collect the data, make sure you cover high and low pitches, and slow and fast speakers.

For the noise and unknown datasets you can either collect this yourself, or make your life a bit easier by using dataset of both 'noise' (all kinds of background noise) and 'unknown' (random words) data that we built for you here: Keyword Spotting Dataset.

To import this data, go to Data acquisition, click the Upload icon, and select a number of 'noise' or 'unknown' samples (there's 25 minutes of each class, but you can select less files if you want), and clicking Begin upload. The data is automatically labeled and added to your project.

Rebalancing your dataset

If you've collected all your training data through the 'Record new data' widget you'll have all your keywords in the 'Training' dataset. This is not great, because you want to keep 20% of your data separate to validate the machine learning model. To mitigate this you can go to Dashboard and select Perform train/test split. This will automatically split your data between a training class (80%) and a testing class (20%). Afterwards you should see something like this:

4. Designing your impulse

With the data set in place you can design an impulse. An impulse takes the raw data, slices it up in smaller windows, uses signal processing blocks to extract features, and then uses a learning block to classify new data. Signal processing blocks always return the same values for the same input and are used to make raw data easier to process, while learning blocks learn from past experiences.

For this tutorial we'll use the "MFCC" signal processing block. MFCC stands for Mel Frequency Cepstral Coefficients. This sounds scary, but it's basically just a way of turning raw audio—which contains a large amount of redundant information—into simplified form. Edge Impulse has many other processing blocks for audio, including "MFE" and the "Spectrogram" blocks for non-voice audio, but the "MFCC" block is great for dealing with human speech.

We'll then pass this simplified audio data into a Neural Network block, which will learn to distinguish between the three classes of audio.

In the Studio, go to the Create impulse tab, add a Time series data, an Audio (MFCC) and a Classification (Keras) block. Leave the window size to 1 second (as that's the length of our audio samples in the dataset) and click Save Impulse.

5. Configure the MFCC block

Now that we've assembled the building blocks of our Impulse, we can configure each individual part. Click on the MFCC tab in the left hand navigation menu. You'll see a page that looks like this:

This page allows you to configure the MFCC block, and lets you preview how the data will be transformed. The right of the page shows a visualization of the MFCC's output for a piece of audio, which is known as a spectrogram. An MFCC spectrogram is a specially tuned spectrogram which highlights frequencies which are common in human speech (Edge Impulse also has normal spectrograms if that's more your thing).

In the spectrogram the vertical axis represents the frequencies (the number of frequency bands is controlled by 'Number of coefficients' parameter, try it out!), and the horizontal axis represents time (controlled by 'frame stride' and 'frame length'). The patterns visible in a spectrogram contain information about what type of sound it represents. For example, the spectrogram in this image shows "Hello world":

And the spectrogram in this image shows "On":

These differences are not necessarily easy for a person to describe, but fortunately they are enough for a neural network to learn to identify.

It's interesting to explore your data and look at the types of spectrograms it results in. You can use the dropdown box near the top right of the page to choose between different audio samples to visualize, or play with the parameters to see how the spectrogram changes.

In addition, you can see the performance of the MFCC block on your microcontroller below the spectrogram. This is the complete time that it takes on a low-power microcontroller (Cortex-M4F @ 80MHz) to analyze 1 second of data.

You might think based on this number that we can only classify 2 or 3 windows per second, but we continuously build up the spectrogram (as it has a time component), which takes less time, and we can thus continuously listen for events 5-6x a second, even on an 40MHz processor. This is already implemented on all fully supported development boards, and easy to implement on your own device.

Feature explorer

The spectrograms generated by the MFCC block will be passed into a neural network architecture that is particularly good at learning to recognize patterns in this type of tabular data. Before training our neural network, we'll need to generate MFCC blocks for all of our windows of audio. To do this, click the Generate features button at the top of the page, then click the green Generate features button. This will take a minute or so to complete.

Afterwards you're presented with one of the most useful features in Edge Impulse: the feature explorer. This is a 3D representation showing your complete dataset, with each data-item color-coded to its respective label. You can zoom in to every item, find anomalies (an item that's in a wrong cluster), and click on items to listen to the sample. This is a great way to check whether your dataset contains wrong items, and to validate whether your dataset is suitable for ML (it should separate nicely).

6. Configure the neural network

With all data processed it's time to start training a neural network. Neural networks are algorithms, modeled loosely after the human brain, that can learn to recognize patterns that appear in their training data. The network that we're training here will take the MFCC as an input, and try to map this to one of three classes—your keyword, noise or unknown.

Click on NN Classifier in the left hand menu. You'll see the following page:

A neural network is composed of layers of virtual "neurons", which you can see represented on the left hand side of the NN Classifier page. An input—in our case, an MFCC spectrogram—is fed into the first layer of neurons, which filters and transforms it based on each neuron's unique internal state. The first layer's output is then fed into the second layer, and so on, gradually transforming the original input into something radically different. In this case, the spectrogram input is transformed over four intermediate layers into just two numbers: the probability that the input represents your keyword, and the probability that the input represents 'noise' or 'unknown'.

During training, the internal state of the neurons is gradually tweaked and refined so that the network transforms its input in just the right ways to produce the correct output. This is done by feeding in a sample of training data, checking how far the network's output is from the correct answer, and adjusting the neurons' internal state to make it more likely that a correct answer is produced next time. When done thousands of times, this results in a trained network.

A particular arrangement of layers is referred to as an architecture, and different architectures are useful for different tasks. The default neural network architecture provided by Edge Impulse will work well for our current project, but you can also define your own architectures. You can even import custom neural network code from tools used by data scientists, such as TensorFlow and Keras (click the three dots at the top of the page).

Before you begin training, you should change some values in the configuration. Change the Minimum confidence rating to 0.6. This means that when the neural network makes a prediction (for example, that there is 0.8 probability that some audio contains "hello world") Edge Impulse will disregard it unless it is above the threshold of 0.6.

Next, enable 'Data augmentation'. When enabled your data is randomly mutated during training. For example, by adding noise, masking time or frequency bands, or warping your time axis. This is a very quick way to make your dataset work better in real life (with unpredictable sounds coming in), and prevents your neural network from overfitting (as the data samples are changed every training cycle).

With everything in place, click Start training. You'll see a lot of text flying past in the Training output panel, which you can ignore for now. Training will take a few minutes. When it's complete, you'll see the Last training performance panel appear at the bottom of the page:

Congratulations, you've trained a neural network with Edge Impulse! But what do all these numbers mean?

At the start of training, 20% of the training data is set aside for validation. This means that instead of being used to train the model, it is used to evaluate how the model is performing. The Last training performance panel displays the results of this validation, providing some vital information about your model and how well it is working. Bear in mind that your exact numbers may differ from the ones in this tutorial.

On the left hand side of the panel, Accuracy refers to the percentage of windows of audio that were correctly classified. The higher number the better, although an accuracy approaching 100% is unlikely, and is often a sign that your model has overfit the training data. You will find out whether this is true in the next stage, during model testing. For many applications, an accuracy above 85% can be considered very good.

The Confusion matrix is a table showing the balance of correctly versus incorrectly classified windows. To understand it, compare the values in each row. For example, in the above screenshot, 96 of the helloworld audio windows were classified as helloworld, while 10 of them were incorrectly classified as unknown or noise. This appears to be a great result.

The On-device performance region shows statistics about how the model is likely to run on-device. Inferencing time is an estimate of how long the model will take to analyze one second of data on a typical microcontroller (an Arm Cortex-M4F running at 80MHz). Peak RAM usage gives an idea of how much RAM will be required to run the model on-device.

7. Classifying new data

The performance numbers in the previous step show that our model is working well on its training data, but it's extremely important that we test the model on new, unseen data before deploying it in the real world. This will help us ensure the model has not learned to overfit the training data, which is a common occurrence.

Fortunately we've put aside 20% of our data already in the 'Test set' (see Data acquisition). This is data that the model has never seen before, and we can use this to validate whether our model actually works on unseen data. To run your model against the test set, head to Model testing, select all items and click Classify selected.

To drill down into a misclassified sample, click the three dots (⋮) next to a sample and select Show classification. You're then transported to the classification view, which lets you inspect the sample, and compare the sample to your training data. This way you can inspect whether this was actually a classification failure, or whether your data was incorrectly labeled. From here you can either update the label (when the label was wrong), or move the item to the training set to refine your model.

8. Deploying to your device

With the impulse designed, trained and verified you can deploy this model back to your device. This makes the model run without an internet connection, minimizes latency, and runs with minimum power consumption. Edge Impulse can package up the complete impulse - including the MFCC algorithm, neural network weights, and classification code - in a single C++ library that you can include in your embedded software.

To export your model, click on Deployment in the menu. Then under 'Build firmware' select your development board, and click Build. This will export the impulse, and build a binary that will run on your development board in a single step. After building is completed you'll get prompted to download a binary. Save this on your computer.

Flashing the device

When you click the Build button, you'll see a pop-up with text and video instructions on how to deploy the binary to your particular device. Follow these instructions. Once you are done, we are ready to test your impulse out.

Running the model on the device

We can connect to the board's newly flashed firmware over serial. Open a terminal and run:

$ edge-impulse-run-impulse --continuous

This will capture audio from the microphone, run the MFCC code, and then classify the spectrogram:

Edge Impulse impulse runner v1.9.1
[SER] Connecting to /dev/tty.usbmodem0004401658161
Predictions (DSP: 143 ms., Classification: 13 ms., Anomaly: 0 ms.):
    helloworld: 	0.98828
    noise: 	        0.0039
    unknown: 	    0.00781

Great work! You've captured data, trained a model, and deployed it to an embedded device. You can now control LEDs, activate actuators, or send a message to the cloud whenever you say a keyword!

Poor performance due to unbalanced dataset?

Is your model working properly in the Studio, but does not recognize your keyword when running in continuous mode on your device? Then this is probably due to dataset imbalance (a lot more unknown / noise data compared to your keyword) in combination with our moving average code to reduce false positives.

When running in continuous mode we run a moving average over the predictions to prevent false positives. E.g. if we do 3 classifications per second you’ll see your keyword potentially classified three times (once at the start of the audio file, once in the middle, once at the end). However, if your dataset is unbalanced (there’s a lot more noise / unknown than in your dataset) the ML model typically manages to only find your keyword in the 'center' window, and thus we filter it out as a false positive.

You can fix this by either:

1. Adding more data :-)

2. Or, by disabling the moving average filter by going into ei_run_classifier.h (in the edge-impulse-sdk directory) and removing:

   Copy

       for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
           result->classification[ix].value =
               run_moving_average_filter(&classifier_maf[ix], result->classification[ix].value);
       }

Note that this might increase the number of false positives the model detects.

9. Conclusion

Congratulations! you've used Edge Impulse to train a neural network model capable of recognizing audible events. There are endless applications for this type of model, from monitoring industrial machinery to recognizing voice commands. Now that you've trained your model you can integrate your impulse in the firmware of your own embedded device, see Running your impulse locally. There are examples for Mbed OS, Arduino, STM32CubeIDE, Zephyr, and any other target that supports a C++ compiler.

Or if you're interested in more, see our tutorials on Continuous motion recognition or Image classification. If you have a great idea for a different project, that's fine too. Edge Impulse lets you capture data from any sensor, build custom processing blocks to extract features, and you have full flexibility in your Machine Learning pipeline with the learning blocks.

We can't wait to see what you'll build! 🚀

In this tutorial, you'll use machine learning to build a system that can recognize when a particular sound is happening—a task known as audio classification. The system you create will be able to recognize the sound of water running from a faucet, even in the presence of other background noise.

You'll learn how to collect audio data from microphones, use signal processing to extract the most important information, and train a deep neural network that can tell you whether the sound of running water can be heard in a given clip of audio. Finally, you'll deploy the system to an embedded device and evaluate how well it works.

At the end of this tutorial, you'll have a firm understanding of how to classify audio using Edge Impulse.

There is also a video version of this tutorial:

You can view the finished project, including all data, signal processing and machine learning blocks here: Tutorial: recognize sounds from audio.

1. Prerequisites

For this tutorial, you'll need a supported device.

If you don't see your supported development board listed here, be sure to check the Hardware specific tutorials page for the appropriate tutorial.

If your device is connected under Devices in the studio you can proceed:

2. Collecting your first data

To build this project, you'll need to collect some audio data that will be used to train the machine learning model. Since the goal is to detect the sound of a running faucet, you'll need to collect some examples of that. You'll also need some examples of typical background noise that doesn't contain the sound of a faucet, so the model can learn to discriminate between the two. These two types of examples represent the two classes we'll be training our model to detect: background noise, or running faucet.

You can use your device to collect some data. In the studio, go to the Data acquisition tab. This is the place where all your raw data is stored, and - if your device is connected to the remote management API - where you can start sampling new data.

Let's start by recording an example of background noise that doesn't contain the sound of a running faucet. Under Record new data, select your device, set the label to noise, the sample length to 1000, and the sensor to Built-in microphone. This indicates that you want to record 1 second of audio, and label the recorded data as noise. You can later edit these labels if needed.

After you click Start sampling, the device will capture a second of audio and transmit it to Edge Impulse. The LED will light while recording is in progress, then light again during transmission.

When the data has been uploaded, you will see a new line appear under 'Collected data'. You will also see the waveform of the audio in the 'RAW DATA' box. You can use the controls underneath to listen to the audio that was captured.

3. Build a dataset

Since you now know how to capture audio with Edge Impulse, it's time to start building a dataset. For a simple audio classification model like this one, we should aim to capture around 10 minutes of data. We have two classes, and it's ideal if our data is balanced equally between each of them. This means we should aim to capture the following data:

• 5 minutes of background noise, with the label "noise"

• 5 minutes of running faucet noise, with the label "faucet"

Real world data

In the real world, there are usually additional sounds present alongside the sounds we care about. For example, a running faucet is often accompanied by the sound of dishes being washed, teeth being brushed, or a conversation in the kitchen. Background noise might also include the sounds of television, kids playing, or cars driving past outside.

It's important that your training data contains these types of real world sounds. If your model is not exposed to them during training, it will not learn to take them into account, and it will not perform well during real-world usage.

For this tutorial, you should try to capture the following:

• Background noise

  • 2 minutes of background noise without much additional activity

  • 1 minute of background noise with a TV or music playing

  • 1 minute of background noise featuring occasional talking or conversation

  • 1 minutes of background noise with the sounds of housework

• Running faucet noise

  • 1 minute of a faucet running

  • 1 minute of a different faucet running

  • 1 minute of a faucet running with a TV or music playing

  • 1 minute of a faucet running with occasional talking or conversation

  • 1 minute of a faucet running with the sounds of housework

It's okay if you can't get all of these, as long as you still obtain 5 minutes of data for each class. However, your model will perform better in the real world if it was trained on a representative dataset.

Data capture and transmission

The amount of audio that can be captured in one go varies depending on a device's memory. The ST B-L475E-IOT01A developer board has enough memory to capture 60 seconds of audio at a time, and the Arduino Nano 33 BLE Sense has enough memory for 16 seconds. To capture 60 seconds of audio, set the sample length to 60000. Because the board transmits data quite slowly, it will take around 7 minutes before a 60 second sample appears in Edge Impulse.

Once you've captured around 10 minutes of data, it's time to start designing an Impulse.

Prebuilt dataset

Alternatively, you can load an example test set that has about ten minutes of data in these classes (but how much fun is that?). See the Running faucet dataset for more information.

4. Design an Impulse

With the training set in place you can design an impulse. An impulse takes the raw data, slices it up in smaller windows, uses signal processing blocks to extract features, and then uses a learning block to classify new data. Signal processing blocks always return the same values for the same input and are used to make raw data easier to process, while learning blocks learn from past experiences.

For this tutorial we'll use the "MFE" signal processing block. MFE stands for Mel Frequency Energy. This sounds scary, but it's basically just a way of turning raw audio—which contains a large amount of redundant information—into simplified form.

We'll then pass this simplified audio data into a Neural Network block, which will learn to distinguish between the two classes of audio (faucet and noise).

In the studio, go to the Create impulse tab. You'll see a Raw data block, like this one.

As mentioned above, Edge Impulse slices up the raw samples into windows that are fed into the machine learning model during training. The Window size field controls how long, in milliseconds, each window of data should be. A one second audio sample will be enough to determine whether a faucet is running or not, so you should make sure Window size is set to 1000 ms. You can either drag the slider or type a new value directly.

Each raw sample is sliced into multiple windows, and the Window increase field controls the offset of each subsequent window from the first. For example, a Window increase value of 1000 ms would result in each window starting 1 second after the start of the previous one.

By setting a Window increase that is smaller than the Window size, we can create windows that overlap. This is actually a great idea. Although they may contain similar data, each overlapping window is still a unique example of audio that represents the sample's label. By using overlapping windows, we can make the most of our training data. For example, with a Window size of 1000 ms and a Window increase of 100 ms, we can extract 10 unique windows from only 2 seconds of data.

Make sure the Window increase field is set to 300 ms. The Raw data block should match the screenshot above.

Next, click Add a processing block and choose the 'MFE' block. Once you're done with that, click Add a learning block and select 'Classification (Keras)'. Finally, click Save impulse. Your impulse should now look like this:

5. Configure the MFE block

Now that we've assembled the building blocks of our Impulse, we can configure each individual part. Click on the MFE tab in the left hand navigation menu. You'll see a page that looks like this:

This page allows you to configure the MFE block, and lets you preview how the data will be transformed. The right of the page shows a visualization of the MFE's output for a piece of audio, which is known as a spectrogram.

The MFE block transforms a window of audio into a table of data where each row represents a range of frequencies and each column represents a span of time. The value contained within each cell reflects the amplitude of its associated range of frequencies during that span of time. The spectrogram shows each cell as a colored block, the intensity which varies depends on the amplitude.

The patterns visible in a spectrogram contain information about what type of sound it represents. For example, the spectrogram in this image shows a pattern typical of background noise:

You can tell that it is slightly different from the following spectrogram, which shows a pattern typical of a running faucet:

These differences are not necessarily easy for a person to describe, but fortunately they are enough for a neural network to learn to identify.

It's interesting to explore your data and look at the types of spectrograms it results in. You can use the dropdown box near the top right of the page to choose between different audio samples to visualize, and drag the white window on the audio waveform to select different windows of data:

There are a lot of different ways to configure the MFCC block, as shown in the Parameters box:

Handily, Edge Impulse provides sensible defaults that will work well for many use cases, so we can leave these values unchanged. You can play around with the noise floor to quickly see the effect it has on the spectrogram.

The spectrograms generated by the MFE block will be passed into a neural network architecture that is particularly good at learning to recognize patterns in this type of tabular data. Before training our neural network, we'll need to generate MFE blocks for all of our windows of audio. To do this, click the Generate features button at the top of the page, then click the green Generate features button. If you have a full 10 minutes of data, the process will take a while to complete:

Once this process is complete the feature explorer shows a visualization of your dataset. Here dimensionality reduction is used to map your features onto a 3D space, and you can use the feature explorer to see if the different classes separate well, or find mislabeled data (if it shows in a different cluster). You can find more information in visualizing complex datasets.

Next, we'll configure the neural network and begin training.

6. Configure the neural network

With all data processed it's time to start training a neural network. Neural networks are algorithms, modeled loosely after the human brain, that can learn to recognize patterns that appear in their training data. The network that we're training here will take the MFE as an input, and try to map this to one of two classes—noise, or faucet.

Click on NN Classifier in the left hand menu. You'll see the following page:

A neural network is composed of layers of virtual "neurons", which you can see represented on the left hand side of the NN Classifier page. An input—in our case, an MFE spectrogram—is fed into the first layer of neurons, which filters and transforms it based on each neuron's unique internal state. The first layer's output is then fed into the second layer, and so on, gradually transforming the original input into something radically different. In this case, the spectrogram input is transformed over four intermediate layers into just two numbers: the probability that the input represents noise, and the probability that the input represents a running faucet.

During training, the internal state of the neurons is gradually tweaked and refined so that the network transforms its input in just the right ways to produce the correct output. This is done by feeding in a sample of training data, checking how far the network's output is from the correct answer, and adjusting the neurons' internal state to make it more likely that a correct answer is produced next time. When done thousands of times, this results in a trained network.

A particular arrangement of layers is referred to as an architecture, and different architectures are useful for different tasks. The default neural network architecture provided by Edge Impulse will work well for our current project, but you can also define your own architectures. You can even import custom neural network code from tools used by data scientists, such as TensorFlow and Keras.

The default settings should work, and to begin training, click Start training. You'll see a lot of text flying past in the Training output panel, which you can ignore for now. Training will take a few minutes. When it's complete, you'll see the Model panel appear at the right side of the page:

Congratulations, you've trained a neural network with Edge Impulse! But what do all these numbers mean?

At the start of training, 20% of the training data is set aside for validation. This means that instead of being used to train the model, it is used to evaluate how the model is performing. The Last training performance panel displays the results of this validation, providing some vital information about your model and how well it is working. Bear in mind that your exact numbers may differ from the ones in this tutorial.

On the left hand side of the panel, Accuracy refers to the percentage of windows of audio that were correctly classified. The higher number the better, although an accuracy approaching 100% is unlikely, and is often a sign that your model has overfit the training data. You will find out whether this is true in the next stage, during model testing. For many applications, an accuracy above 80% can be considered very good.

The Confusion matrix is a table showing the balance of correctly versus incorrectly classified windows. To understand it, compare the values in each row. For example, in the above screenshot, all of the faucet audio windows were classified as faucet, but a few noise windows were misclassified. This appears to be a great result though.

The On-device performance region shows statistics about how the model is likely to run on-device. Inferencing time is an estimate of how long the model will take to analyze one second of data on a typical microcontroller (here: an Arm Cortex-M4F running at 80MHz). Peak memory usage gives an idea of how much RAM will be required to run the model on-device.

7. Classifying new data

The performance numbers in the previous step show that our model is working well on its training data, but it's extremely important that we test the model on new, unseen data before deploying it in the real world. This will help us ensure the model has not learned to overfit the training data, which is a common occurrence.

Edge Impulse provides some helpful tools for testing our model, including a way to capture live data from your device and immediately attempt to classify it. To try it out, click on Live classification in the left hand menu. Your device should show up in the 'Classify new data' panel. Capture 5 seconds of background noise by clicking Start sampling:

The sample will be captured, uploaded, and classified. Once this has happened, you'll see a breakdown of the results:

Once the sample is uploaded, it is split into windows–in this case, a total of 41. These windows are then classified. As you can see, our model classified all 41 windows of the captured audio as noise. This is a great result! Our model has correctly identified that the audio was background noise, even though this is new data that was not part of its training set.

Of course, it's possible some of the windows may be classified incorrectly. Since our model was 99% accurate based on its validation data, you can expect that at least 1% of windows will be classified wrongly—and likely much more than this, since our validation data doesn't represent every possible type of background or faucet noise. If your model didn't perform perfectly, don't worry. We'll get to troubleshooting later.

8. Model testing

Using the Live classification tab, you can easily try out your model and get an idea of how it performs. But to be really sure that it is working well, we need to do some more rigorous testing. That's where the Model testing tab comes in. If you open it up, you'll see the sample we just captured listed in the Test data panel:

In addition to its training data, every Edge Impulse project also has a test dataset. Samples captured in Live classification are automatically saved to the test dataset, and the Model testing tab lists all of the test data.

To use the sample we've just captured for testing, we should correctly set its expected outcome. Click the ⋮ icon and select Edit expected outcome, then enter noise. Now, select the sample using the checkbox to the left of the table and click Classify selected:

You'll see that the model's accuracy has been rated based on the test data. Right now, this doesn't give us much more information that just classifying the same sample in the Live classification tab. But if you build up a big, comprehensive set of test samples, you can use the Model testing tab to measure how your model is performing on real data.

Ideally, you'll want to collect a test set that contains a minimum of 25% the amount of data of your training set. So, if you've collected 10 minutes of training data, you should collect at least 2.5 minutes of test data. You should make sure this test data represents a wide range of possible conditions, so that it evaluates how the model performs with many different types of inputs. For example, collecting test audio for several different faucets is a good idea.

You can use the Data acquisition tab to manage your test data. Open the tab, and then click Test data at the top. Then, use the Record new data panel to capture a few minutes of test data, including audio for both background noise and faucet. Make sure the samples are labelled correctly. Once you're done, head back to the Model testing tab, select all the samples, and click Classify selected:

The screenshot shows classification results from a large number of test samples (there are more on the page than would fit in the screenshot). The panel shows that our model is performing at 85% accuracy, which is 5% less than how it performed on validation data. It's normal for a model to perform less well on entirely fresh data, so this is a successful result. Our model is working well!

For each test sample, the panel shows a breakdown of its individual performance. For example, one of the samples was classified with only 62% accuracy. Samples that contain a lot of misclassifications are valuable, since they have examples of types of audio that our model does not currently fit. It's often worth adding these to your training data, which you can do by clicking the ⋮ icon and selecting Move to training set. If you do this, you should add some new test data to make up for the loss!

Testing your model helps confirm that it works in real life, and it's something you should do after every change. However, if you often make tweaks to your model to try to improve its performance on the test dataset, your model may gradually start to overfit to the test dataset, and it will lose its value as a metric. To avoid this, continually add fresh data to your test dataset.

9. Model troubleshooting

If the network performed great, fantastic! But what if it performed poorly? There could be a variety of reasons, but the most common ones are:

1. The data does not look like other data the network has seen before. This is common when someone uses the device in a way that you didn't add to the test set. You can add the current file to the test set by adding the correct label in the 'Expected outcome' field, clicking ⋮, then selecting Move to training set.

2. The model has not been trained enough. Increase number of epochs to 200 and see if performance increases (the classified file is stored, and you can load it through 'Classify existing validation sample').

3. The model is overfitting and thus performs poorly on new data. Try reducing the number of epochs, reducing the learning rate, or adding more data.

4. The neural network architecture is not a great fit for your data. Play with the number of layers and neurons and see if performance improves.

As you see, there is still a lot of trial and error when building neural networks. Edge Impulse is continually adding features that will make it easier to train an effective model.

10. Deploying to your device

With the impulse designed, trained and verified you can deploy this model back to your device. This makes the model run without an internet connection, minimizes latency, and runs with minimum power consumption. Edge Impulse can package up the complete impulse - including the MFE algorithm, neural network weights, and classification code - in a single C++ library that you can include in your embedded software.

To export your model, click on Deployment in the menu. Then under 'Build firmware' select your development board, and click Build. This will export the impulse, and build a binary that will run on your development board in a single step. After building is completed you'll get prompted to download a binary. Save this on your computer.

Flashing the device

When you click the Build button, you'll see a pop-up with text and video instructions on how to deploy the binary to your particular device. Follow these instructions. Once you are done, we are ready to test your impulse out.

Running the model on the device

We can connect to the board's newly flashed firmware over serial. Open a terminal and run:

$ edge-impulse-run-impulse

This will capture audio from the microphone, run the MFE code, and then classify the spectrogram:

Starting inferencing in 2 seconds...
Recording
Recording OK
Predictions (DSP: 399 ms., Classification: 175 ms., Anomaly: 0 ms.):
    faucet: 0.03757
    noise: 0.96243
Starting inferencing in 2 seconds...

Great work! You've captured data, trained a model, and deployed it to an embedded device. It's time to celebrate—by pouring yourself a nice glass of water, and checking whether the sound is correctly classified by you model.

11. Conclusion

Congratulations! you've used Edge Impulse to train a neural network model capable of recognizing a particular sound. There are endless applications for this type of model, from monitoring industrial machinery to recognizing voice commands. Now that you've trained your model you can integrate your impulse in the firmware of your own embedded device, see Running your impulse locally. There are examples for Mbed OS, Arduino, STM32CubeIDE, and any other target that supports a C++ compiler.

Or if you're interested in more, see our tutorials on Continuous motion recognition or Image classification. If you have a great idea for a different project, that's fine too. Edge Impulse lets you capture data from any sensor, build custom processing blocks to extract features, and you have full flexibility in your Machine Learning pipeline with the learning blocks.

We can't wait to see what you'll build! 🚀