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. As Google Research scientists John Hershey, Scott Wisdom, and Hakan Erdogan explain in a blog post, the bulk of sound separation models assume the number of sounds in a mixture to be static, and they either separate mixtures of a small number of sound types (such as speech versus nonspeech) or different instances of the same sound type (like a first speaker versus a second speaker).