Audio Pattern Recognition - Why it is Important and How it is Used

Thanks to the arrival and improvement of audio pattern recognition technology in machine learning and big data, we now have access to more audio data than ever before. In this post, we explain what audio pattern recognition is, how it works and then show you some real world examples of it in action.

These new pattern recognition technologies have made it possible to have data that was normally speculated upon or deduced, rather than known for sure.

Because this data is now from more credible and trusted sources, it opens up the opportunity for more in-depth and comprehensive methods of analysing actual audio data and being used for tasks such as:

• sound event detection
• audio tagging
• music classification
• acoustic scene classification
• speech emotion detection and classification

Sound pattern recognition is one of the most important tools used in big data and helps to get to the heart of the data and what it means.

How Does Sound Pattern Recognition Work?

Sound recognition (and speech recognition) works using specially developed algorithms to recognise patterns in audio signals.

The algorithm then determines and compartmentalises data according to specific pre-set criteria or using shared elements and components.

The algorithm also allows for learning and the chance to improve things, which is crucial to machine learning technology providing more and more accurate results.

The most simplistic way to explain it is right there in its name, it’s used to find sound patterns in collections of audio data.

Patterns that tell stories about the data through a series of flat lines and spikes.

The data that can be assessed using pattern recognition tech can be anything, including:

• Sentiment
• Images
• Text
• Speech

For this post, we are going to focus heavily on the data being sounds and audio.

Techniques Involved in Pattern Recognition

There are 3 main techniques used in audio pattern recognition. These are:

• Template Matching – this matches the features of the data to a pre-recorded and defined template. It identifies the data via proxy.
• Structural/Syntactic – this form of audio pattern recognition helps to define more comprehensive relationships between different elements and components, like parts of speech or audio. This sound and speech recognition technique involves machine learning that is known as semi-supervised.
• Statistical – this is used to identify the place where a specific piece of data belongs and involves machine learning that is supervised.

More About Audio Pattern Recognition Algorithms

It’s fair to say that most pattern recognition used in AI operations is easy to describe, but there is so much more to it all.

When it comes to pattern recognition algorithms, there are 2 main, important parts:

• Descriptive – this part of the algorithm compartmentalises commonalities in a specific way
• Explorative – this part is used to identify those commonalities

When these two components are used in conjunction, all the important extract insights are taken from the data collected.

When the different pieces of data that they have in common are found and the algorithm learns how they correlate , subject details are found that could be crucial to understanding the data better.

Audio Pattern Recognition Use Cases

As we have already established, there are various types of data pattern recognition can be used to assess, but for the basis of this post, we are focusing still on the audio side of things.

Sound is just as important an information source as others. As a result of the quick evolution and improvement of the algorithms used in machine learning, companies and organisations were able to use it for providing some basic, but vital services.

Essentially, voice recognition is based on almost identical principles as another form of pattern recognition known as Optical Character Recognition or OCR. The difference between the two is information source.

Some of the main uses of sound, voice and other forms of audio recognition includes:

• Personal Assistant and Artificial Intelligence Assistants apps - These rely on a natural form of speech recognition and language processing for the composition of messages and utilise an additional sound sample database to actually provide the audio version of the message.
• Diagnosis Based on Sound – This involves the use of a comparative sounds database to pick out any anomalies and then provide the probable cause and how to repair it. This is most commonly used by the automobile manufacturing industry to help assess the condition of different parts of vehicles including the engine.
• Text-to-Speech and Speech-to-Text transformations – these use a special speech recognition and generation engine. They also use an OCR engine along with a comparative samples database. Aside from being used in AI assistants, this form of audio and speech recognition is also utilised when written text needs to be narrated.
• Automatic Captions – this use case utilises speech-to-text recognition along with images overlaid to provide accurate text on screens (like Facebook and YouTube auto subtitling features, for example).

Sentiment Analysis

This is a subsection of the larger practice of pattern recognition and we’ll delve a little further to define what it is and what it means.

Essentially, sentiment analysis is used to help appreciate and understand what the driving intent is, opinion and even mood is behind the words.

There are not many forms of pattern recognition that are more sophisticated than sentiment analysis.

For businesses, this can be utilised in the exploration of various reactions to interactions involving different types of platforms.

In order to achieve this, the system makes use of unsupervised machine learning along with the foundational recognition procedures.

When it comes to the assumptions made through sentiment analysis, it is usually from very reputable and ground sources like dictionaries, and can possibly involve databases that are more customized depending on what the operation context actually is.

Sentiment Analysis Use Cases

To make it more practical for you, here are various sentiment analysis use cases, including:

• Customer Relationship, Content Optimisation, Audience Research Platforms – this use case is utilised to further define audience segments, how they interact with the content and assessment of the sentiments behind them. It is also contributory to improved content optimisation.
• Service Support – speech recognition that assists with defining the nature of queries (whether poorly defined, combative, negative or positive). This is regularly used in AI Assistants like Cortana, Siri and Alexa.
• Recommendation/Prescription – Perhaps the example that most will be familiar of with this is Amazon and their “People Also Buy” and Netflix with the “You Might Also Like” features. This is used in the prediction of what content will be of interest to particular end users. Provided recommendations and suggestions could be enhanced by looking at their past history using a service and the queries.

By now, you’ll recognise how deeply engrained audio pattern recognition is becoming into our daily lives. The benefits of this technology extend to both the end consumer and the content producers to give us the most optimal products and services possible.

To talk to our team about pattern recognition or our audio watermarking technology, contact us for a demo today.