Abstract: A computer-implemented method for upmixing audiovisual data can include obtaining audiovisual data including input audio data and video data accompanying the input audio data. Each frame of the video data can depict only a portion of a larger scene. The input audio data can have a first number of audio channels. The computer-implemented method can include providing the audiovisual data as input to a machine-learned audiovisual upmixing model. The audiovisual upmixing model can include a sequence-to-sequence model configured to model a respective location of one or more audio sources within the larger scene over multiple frames of the video data. The computer-implemented method can include receiving upmixed audio data from the audiovisual upmixing model. The upmixed audio data can have a second number of audio channels. The second number of audio channels can be greater than the first number of audio channels.

Abstract: A computer-implemented method for upmixing audiovisual data can include obtaining audiovisual data including input audio data and video data accompanying the input audio data. Each frame of the video data can depict only a portion of a larger scene. The input audio data can have a first number of audio channels. The computer-implemented method can include providing the audiovisual data as input to a machine-learned audiovisual upmixing model. The audiovisual upmixing model can include a sequence-to-sequence model configured to model a respective location of one or more audio sources within the larger scene over multiple frames of the video data. The computer-implemented method can include receiving upmixed audio data from the audiovisual upmixing model. The upmixed audio data can have a second number of audio channels. The second number of audio channels can be greater than the first number of audio channels.

Abstract: Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.

Abstract: Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.

Abstract: The process of implementing a belief propagation network in software and/or hardware can begin with a factor-graph-designer who designs a factor graph that implements that network. A development system provides a user with a way to specify a factor graph at a high or abstract level, and then solve the factor graph, or make an instance of the factor graph in software and/or hardware based on the specification. Factor graphs enable designers to create a graphical model of complicated belief propagation networks such as Markov chains, hidden Markov models, and Bayesian networks.

Abstract: Some general aspects relate to systems and methods of analog computation using numerical representation with uncertainty. For example, a specification of a group of variables is accepted, with each variable having a set of at least N possible values. The group of variables satisfies a set of one or more constraints, and each variable is specified as a decomposition into a group of constituents, with each constituent having a set of M (e.g., M<N) possible constituent values that can be determined based on the variable values. The method also includes forming a specification for configuring a computing device that implements a network representation of the constraints based on the specification of the group of variables. The network representation includes a first set of nodes corresponding to the groups of constituents, a second set of nodes corresponding to the set of constraints, and interconnections between the first and the second sets of nodes for passing continuous-valued data.

Abstract: The process of implementing a belief propagation network in software and/or hardware can begin with a factor-graph-designer who designs a factor graph that implements that network. A development system provides a user with a way to specify a factor graph at a high or abstract level, and then solve the factor graph, or make an instance of the factor graph in software and/or hardware based on the specification. Factor graphs enable designers to create a graphical model of complicated belief propagation networks such as Markov chains, hidden Markov models, and Bayesian networks.

Abstract: Some general aspects relate to systems and methods of analog computation using numerical representation with uncertainty. For example, a specification of a group of variables is accepted, with each variable having a set of at least N possible values. The group of variables satisfies a set of one or more constraints, and each variable is specified as a decomposition into a group of constituents, with each constituent having a set of M (e.g., M<N) possible constituent values that can be determined based on the variable values. The method also includes forming a specification for configuring a computing device that implements a network representation of the constraints based on the specification of the group of variables. The network representation includes a first set of nodes corresponding to the groups of constituents, a second set of nodes corresponding to the set of constraints, and interconnections between the first and the second sets of nodes for passing continuous-valued data.