Deep Collector
Automatically build a face recognition dataset
How can I automatically build training datasets from my media footage?
use case: Identification of speakers
the challenge
Face recognition is already widely used today, but there are still many formats in which a face cannot always be recognized, such as audio-only files. In this case, it is essential to be able to reliably identify speakers based on their audio characteristics.
How can DeepVA help me to store individual speakers as a data set in order to structure my image material?
the solution
With Deep VA’s Deep Customizer you can individualize the speaker recognition with your own personalities. Simply upload one or more audio files of the personality and title the speaker’s name. Expanding and Individualizing your speaker database couldn’t be easier.
Since training data is one of the most important elements in the use of AI, we offer data set functions to manage your training data transparently, intuitively and efficiently while respecting data privacy. The system can be used on-premise or in the cloud. If it is to be part of a workflow, integration is required. Via our RESTful API it can be easily integrated in any existing system or workflow.
What results can be obtained?
Build a speaker database without effort, using a very intuitive and easy to use system
Create a completely unique speaker database exactly for your purpose
faster data acquisition
COST REDUCTION
faster labelling
identification of speakers
In just a few seconds, new faces are trained using our Face Training and can be used in the Recognition Services.
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Related use cases
How can I automatically build training datasets from my media footage?
How can I recognize less-known or regional people in my footage?
How can I build up a dataset for my speaker recognition?
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Designed and engineered in Germany.
Aiconix GmbH
Wannerstraße 19
79106 Freiburg
Germany
The project has indirectly received funding from the European Commission’s Horizon 2020 Framework Programme through the STADIEM project (Grant Agreement 957321)
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