Preparing Audio Data for a Custom Machine Learning Model

AI is improving processes across the board for businesses measuring sound. Smart companies in this sector are already integrating off-the-shelf solutions into their business. Certain companies may find, however, they hav...

Preparing Audio Data for a Custom Machine Learning Model

AI is improving processes across the board for businesses measuring sound. Smart companies in this sector are already integrating off-the-shelf solutions into their business. Certain companies may find, however, they have specific data requirements. This means off-the-shelf models simply won’t work. The solution? A custom machine-learning model. In this guide, we will explore how you can effectively collect and prepare your specific data for the development of a tailored AI model. By following these steps, you’ll be equipped to leverage the full potential of AI in sound recognition

Step 1: Define the Problem

The first and most crucial step is to understand and define the problem clearly. What sounds are you trying to identify? Are there any specific features of these sounds that are particularly important? How will you use the results? 
For instance, you might need to differentiate between different types of machinery noise or detect a rare sound on an airfield. The more clearly you can define your problem, the better equipped you’ll be to collect relevant data. Ultimately this will result in the best training for a successful model. Once you know what sounds you need to identify, look at what capabilities you require. Maybe you need to assign descriptive labels to acoustic events or identify specific events of interest in an audio recording.
Knowing exactly what you need from the technology is the most important first step. 

Step 2: Collect and Prepare the Data

Once you have defined the problem, the next step is to collect a robust dataset of audio files. 
When collecting audio files, try to ensure a diverse representation of sounds. For instance, if you’re training a model to recognise engine sounds, try to collect recordings of that sound under a variety of conditions. For example, recordings from different times of day, in different weather conditions, and so on. This diversity can help your model perform more accurately and reliably in the real world.

Gather the Data

Data plays a crucial role in training an accurate and effective machine learning model. Consider the sources and environments from which you want to collect the data. Then, ensure that they align with the context in which your model will be deployed. Once you’ve done that, collect and tag your data with relevant labels where possible- especially with niche sounds.

Consider the Context

Undertsanding the context in which your sound recognition model will be deployed is essential. Different environments and scenarios may introduce unique challenges or variations in sound patterns. Take into account the specific context of your application, such as different locations, time periods, or environmental conditions, when gathering and curating your dataset. This contextual information will contribute to training a more robust and reliable machine learning model.
Once you’ve collected your audio data, it needs to be prepared for training. This typically involves several steps that we take care of:

Step 3: Train the Machine Learning Model


If your audio files are in different formats, they may need to be converted to a common format. Most machine learning libraries can handle common formats like WAV or MP3.


 If your audio files are long, they may need to be segmented into shorter clips. These clips should be long enough to contain the complete sound you’re trying to identify, but not so long that they contain a lot of irrelevant noise.


The volume levels of your audio files should be roughly equalised. This helps prevent the model from mistakenly associating loudness with any particular sound.

Feature Extraction

In many cases, it can be helpful to extract specific features from your audio files, rather than feeding the raw audio data into the model. Features might include spectral characteristics, tempo, pitch, or other attributes. Feature extraction can help your model learn more efficiently and accurately.

Step 4: Label the Data

Each of your audio samples needs to be labelled with its corresponding outcome or label. This is a critical step, as it’s this labelled data that your model will learn from. The labels will depend on the problem you’re trying to solve – if you’re identifying construction sounds for example, each label might be the names of different equipment. 

  1. Annotation Design: Design an annotation scheme that defines the specific sound categories you want the model to recognise. For example, your annotation scheme may include labels like “engine revving,” “glass breaking,” “tyres screeching,” etc.
  2. Manual Labelling: If your data is unlabelled, it’s necessary to manually attach labels. This provides the machine-learning model with a data bank. With these manual annotations, the AI will use auto-machine learning to automatically assign these labels in the future.
  3. Quality Assurance: Implement quality assurance measures to ensure accurate annotations. Perform regular checks and inter-annotator agreement assessments to evaluate consistency. Resolve any discrepancies or ambiguities through discussions or clarifications.

Once you’ve defined your problem, collected and prepared your data, and labelled it accurately, we can move on to the next step: training your custom machine learning model.

Final Thoughts 

Data is the lifeblood of machine learning. It forms the bedrock of our endeavours, enabling our models to thrive. While off-the-shelf solutions have their merits, companies that follow the steps outlined in this guide can effectively collect and prepare unique data to develop tailored AI models. By doing so, they can fully leverage the power of AI, achieve greater accuracy, make informed decisions, optimise processes, and gain a competitive advantage in the market. Embracing custom models opens up new possibilities for innovation and expansion, ensuring businesses stay ahead in the evolving landscape of sound measurement.

By Johanna Walsh

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