Our scalable technology platform
Data collection – It’s the foundation
To teach our technology to recognise sounds, we have to expose it to high quality, real-world data. Quantity matters, but it is also about relevance and diversity.
We record audio events and acoustic scenes either in our dedicated Sound Labs, through our network of volunteers, or via our dedicated data collection team.
Alexandria™ – The world’s largest, commercially-exploitable audio dataset
Sound recognition was a zero-data problem when we started this journey.
We built Alexandria™, a dedicated ML-ready audio dataset, which is used to train our sound recognition algorithms.
Alexandria™ contains millions of labelled, relevant sound events and acoustic scene data. All audio data is expertly labelled, with full data provenance built in from the start. Our dataset is structured according to our unique taxonomy, encompassing anthrophonic, biophonic and geophonic sounds.
Alexandria™ contains 15,000,000 labelled sound events across over 700 label types.
AuditoryNET™ – Our specialised deep neural network for sound recognition
Intelligent sound recognition requires a deep knowledge of the ideophonic features of sounds. It is the only way to teach machines how to hear. We’ve built our own highly-optimised and dedicated deep neural network that accurately models sounds based on their ideophonic features.
ai3™ – Our proven, lightweight and flexible software platform
Our customers license ai3™ which gives consumer products a sense of hearing.
Because our DNN is dedicated to sound recognition, it is extremely compact, which makes it perfect for a wide range of products from smart speakers to hearables.
Sounds are fundamentally different from voice and music
Speech recognition and wake words are limited by the type of sounds that the human mouth can produce, as well as conditioned by the communicative structure of human language, which can both be exhaustively mapped.
Similarly, music mostly results from physical resonance, and is conditioned by the rules of various musical genres.
So whilst the human ear and brain are very good at interpreting sounds in spite of acoustic variations, computers were originally designed to process repeatable tasks. Thus, teaching a machine how to recognise speech and music greatly benefits from such pre-defined rules and prior knowledge.
Sounds, on the other hand, can be much more diverse, unbounded and unstructured than speech and music.
Think about a window being smashed, and all the different ways glass shards can hit the floor randomly, without any particular intent or style. Or think about the difference between a long baby cry and a short dog bark, or the relative loudness of a naturally spoken conversation versus an explosive glass crash.
Now you understand why sound recognition required us to develop a special kind of expertise: collecting sound data ourselves and tackling real-world sound recognition problems made us pioneering experts in understanding the full extent of sound variability.
Sound recognition for a wide range of products
Expertise in data collection, the world's leading audio dataset for machine learning and a highly specialised DNN enables us to create ai3™ - a flexible software platform capable of detecting a large number of sounds in a wide range of devices.
Window glass break
Acoustic scene 1
Acoustic scene 2
Acoustic scene 3
Acoustic scene 4
Introducing the Polyphonic Sound Detection Score
A robust evaluation framework and metric for sound recognition
We have a portfolio of 25 patents (filed and granted), covering the uses of sound recognition in products as well as the specialist techniques that we use to give machines a sense of hearing.
The Cortex M0+ challenge: How low can we go?
So we set ourselves a really complicated hardware challenge with exciting potential…. to see if we can embed our class-leading software on the M0+ based chip.