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AI can be trained to accurately recognise specific sounds, whether it be precise process noises, or distinct human vocal emotions.
Being able to automatically recognise patterns, tones, frequencies, pitch and variabilities in sounds and voices is an extremely powerful tool in a wide range of businesses, operations, tasks and processes.
By learning from known and highly variable sound and voice characteristics including all the in-between highly variable and unpredictable acoustic attributes; sound and voice recognitions becomes more insightful, more meaningful, more accurate and much more beneficial when used for tasks and processes like: e.g. sound classification, automatic and rapid sound-based diagnosis, anomaly detection, quality control, computer hearing, behavioural analysis, investigations, self-driving vehicles, threat assessment, robotics, security, and responding to audible changes in any type of dynamic, moving environment, amongst many more cases.
The accuracy of sound and voice recognition is directly proportional to what is already known about certain sounds and voices (e.g. their full range of acoustic attributes): the more past voices and sounds to learn from the more accurate and informed we can conduct future voice and sound recognition.
Learning from past voices and sounds in order to recognise the same type of audible stimuli in the future might sound straightforward - it is of course exactly what a human brain does all the time - but making this process automatic and asking a machine to do this is extremely complex and demanding, mainly due to the huge amount of variability involved in acoustics: pitch, volume, frequency, amplitude, range, modulation etc - and these are variations that can happen for just one sound, let alone when you have numerous sounds in the same environment.
If a traditional software programming approach was used, the computer would continually be asking "if this, then learn that; if x and y, learn z". This stepwise methodology is extremely limited, inefficient and resource-heavy as the program will only do what it has explicitly been told to look for by the programmer; making it next to impossible to account for all possibilities and variations, even in the simplest of sounds. You certainly couldn't use this approach for complex and diverse scenarios e.g. multiple and variable voices and sounds in the same time or space.
With AI and Machine Learning technology it is now possible to learn from near infinite amounts of variable, constantly changing voice and sound data in order to carry out much more accurate sound recognition, automatically.
ELDR-I Sound is built around our powerful ELDR-I AI Engine, which is a Deep Learning Convolutional Neural Network. ELDR-I Sound uses Supervised Learning and Sound/Voice Classification to learn how to recognise all types of voices and sounds thrown at it.
When ELDR-I Sound has learnt (trained) from the data, it is then primed to receive current-status voice and sound data in real time, or from a recording from which to rapidly and accurately recognise/classify sounds within - in order to give a response - and that response can range from a simple classification to a "yes/no" to triggering sophisticated downstream events.
ELDR-I Sound can handle and learn from multiple sources, sizes and complexities of voice and sound data for numerous environments and requirements simultaneously. Data can be changed at any time and it can continually learn.
By default ELDR-I Sound is plug and play - you can simply give it appropriately formatted acoustic data and it will automatically learn from it, including self optimisation, self scaling and classification.
In some cases you may be happy with plug and play, however almost everything in ELDR-I Sound is configurable; from labelling of images, to colours, displays, output format, learning modes, learning accuracy, all the way through to Convolutional Neural Network dynamics and dimensions.
ELDR-I Sound uses a rich intuitive GUI Dashboard from which to manage the whole AI process (sound and voice data preparation, learning, outputting and testing), including a comprehensive suite of gamified charts and other visual displays to monitor everything.
AI Integration is our speciality. We understand that AI can be used in a variety of ways and in numerous system-types and processes. We build our software to be entirely modular and there are multiple integration methods and points ranging from network-based RESTful API integration to direct coupling at the code level, depending on the response time required, amongst other considerations.
As well as Voice and Sound Recognition software for the Finance industry, we provide a comprehensive set of other Artificial Intelligence, Machine Learning, Deep Learning and Data Science software:
Whether you are starting out on your first AI project, just interested in the possibilities of AI or are wanting to expand your existing AI suite, we are here to help.
We will discuss with you where you are, where you want to be, and how we can achieve it with AI - whether by a bespoke solution or using one of our off-the-shelf products
We will work with you to gather, analyse and prepare all your relevant data sources for use in the AI system(s)
We will run and tune the AI throughout the AI learning process and enable the AI to produce a real time visual output to confirm the AI is producing beneficial results
When you are satisfied the AI is delivering the results you desire, we will integrate the AI with your new or existing systems
Fennaio has the expertise in the Finance sector to get you up and running with Voice and Sound Recognition AI and Machine Learning in your new or existing systems, software and operations.Get Started