Dolby International Ab

Dolby Laboratories Licensing Corporation

Joan Serra

Santiago Pascual

Jordi Pons Puig

Christian James Steinmetz

Described is a method of training a neural-network-based system for determining an indication of an audio quality of an audio input. The method includes obtaining, as input, at least one training set comprising audio samples. The audio samples include audio samples of a first type and audio samples of a second type, wherein each of the first type of audio samples is labelled with information indicative of a respective predetermined audio quality metric, and wherein each of the second type of audio samples is labelled with information indicative of a respective audio quality metric relative to that of a reference audio sample. The method further includes: inputting the training set to the neural-network-based system; and iteratively training the system to predict the respective label information of the audio samples in the training set.

Method for learning an audio quality metric combining labeled and unlabeled data

A deep-learning-based system for performing automated multitrack mixing based on a plurality of input audio tracks is described herein. The system comprises one or more instances of a deep-learning-based first network and one or more instances of a deep-learning-based second network. Particularly, the first network is configured to, based on theinput audio tracks, generate parameters for use in the automated multitrack mixing. The second network is configured to, based on the parameters, apply signal processing and at least one mixing gain to the input audio tracks, for generating an output mix of the audio tracks.

System for automated multitrack mixing

The present disclosure relates to a method and system for performing packet loss concealment using a neural network system. The method comprises obtaining a representation of an incomplete audio signal, inputting the representation of the incomplete audio signal to an encoder neural network and outputting a latent representation of a predicted complete audio signal. The latent representation is input to a decoder neural network which outputs a representation of a predicted complete audio signal comprising a reconstruction of the original portion of the complete audio signal, wherein said encoder neural network and said decoder neural network have been trained with an adversarial neural network.

Growing community of inventors

Barcelona, Spain

Joan Serra