Google open-sources data set to train and benchmark AI sound separation models

Venturebeat | April 09, 2020

Google open-sources data set to train and benchmark AI sound separation models
Google today announced the release of a new data set — the Free Universal Sound Separation data set, or FUSS for short — intended to support the development of AI models that can separate distinct sounds from recording mixes. The use cases are potentially endless, but if it were to be commercialized, FUSS could be used in corporate settings to extract speech from conference calls. It follows on the heels of a study by Google and the Idiap Research Institute in Switzerland describing two machine learning models — a speaker recognition network and a spectrogram masking network — that together “significantly” reduced the speech recognition word error rate (WER) on multispeaker signals. Elsewhere, tech giants including Alibaba and Microsoft have invested significant time and resources in solving the sound separation problem.

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In a digital-first world, OpenText solutions for Microsoft® Office 365™ help organizations support their digital transformation initiatives, while meeting their compliance and information governance requirements within Microsoft’s productivity cloud. By using OpenText, customers can manage and archive the growing volume of content from both Microsoft and non-Microsoft data sources, as well as significantly reduce ongoing administration and storage costs. More importantly, they are able to implement private, hybrid, and public cloud environments to provide information in business context,whenever and wherever it is needed.

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