Abstract: A computer may segment a noisy audio signal into audio frames and execute a deep neural network (DNN) to estimate an instantaneous function of clean speech spectrum and noisy audio spectrum in the audio frame. This instantaneous function may correspond to a ratio of an a-priori signal to noise ratio (SNR) and an a-posteriori SNR of the audio frame. The computer may add estimated instantaneous function to the original noisy audio frame to output an enhanced speech audio frame.

Abstract: Described herein are systems and methods for improved audio analysis using a computer-executed neural network having one or more in-network data augmentation layers. The systems described herein help ease or avoid unwanted strain on computing resources by employing the data augmentation techniques within the layers of the neural network. The in-network data augmentation layers will produce various types of simulated audio data when the computer applies the neural network on an inputted audio signal during a training phase, enrollment phase, and/or testing phase. Subsequent layers of the neural network (e.g., convolutional layer, pooling layer, data augmentation layer) ingest the simulated audio data and the inputted audio signal and perform various operations.

Abstract: Systems, asset tag devices, and methods are described herein to provide asset, or object, tracking using a plurality of beacons. The asset tag devices may be affixed, or coupled to, the asset, or object, and may receive beacon advertising packets from the beacons, and then transmit, or broadcast, tag advertising packets including information from the received beacon advertising packets, which may be used to determine an approximate location of the asset tag device, and in turn, the asset or object.

Abstract: Disclosed are systems and methods including computing-processes executing machine-learning architectures for voice biometrics, in which the machine-learning architecture implements one or more language compensation functions. Embodiments include an embedding extraction engine (sometimes referred to as an “embedding extractor”) that extracts speaker embeddings and determines a speaker similarity score for determine or verifying the likelihood that speakers in different audio signals are the same speaker. The machine-learning architecture further includes a multi-class language classifier that determines a language likelihood score that indicates the likelihood that a particular audio signal includes a spoken language. The features and functions of the machine-learning architecture described herein may implement the various language compensation techniques to provide more accurate speaker recognition results, regardless of the language spoken by the speaker.

Abstract: Disclosed are systems and methods including computing-processes executing machine-learning architectures implementing label distribution loss functions to improve age estimation performance and generalization. The machine-learning architecture includes a front-end neural network architecture defining a speaker embedding extraction engine of the machine-learning architecture, and a backend neural network architecture defining an age estimation engine of the machine-learning architecture. The embedding extractor is trained to extract low-level acoustic features of a speaker's speech, such as mel-frequency cepstral coefficients (MFCCs), from audio signals, and then extract a feature vector or speaker embedding vector that mathematically represents the low-level features of the speaker. The age estimator is trained to generate an estimated age for the speaker and a Gaussian probability distribution around the estimated age, by applying the various types of layers of the age estimator on the speaker embedding.

Abstract: Embodiments described herein provide for a machine-learning architecture system that enhances the speech audio of a user-defined target speaker by suppressing interfering speakers, as well as background noise and reverberations. The machine-learning architecture includes a speech separation engine for separating the speech signal of a target speaker from a mixture of multiple speakers' speech, and a noise suppression engine for suppressing various types of noise in the input audio signal. The speaker-specific speech enhancement architecture performs speaker mixture separation and background noise suppression to enhance the perceptual quality of the speech audio. The output of the machine-learning architecture is an enhanced audio signal improving the voice quality of a target speaker on a single-channel audio input containing a mixture of speaker speech signals and various types of noise.

Abstract: Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and/or continuously, in some cases in addition to actively and/or statically, enrolling speakers as the speakers speak into or around an edge device (e.g., car, television, radio, phone). The system identifies users on the fly without requiring a new speaker to mirror prompted utterances for reconfiguring operations. The system manages speaker profiles as speakers provide utterances to the system. Machine-learning architectures implement a passive and continuous voice biometrics system, possibly without knowledge of speaker identities. The system creates identities in an unsupervised manner, sometimes passively enrolling and recognizing known or unknown speakers.

Abstract: Embodiments described herein provide for systems and methods for voice-based cross-channel enrollment and authentication. The systems control for and mitigate against variations in audio signals received across any number of communications channels by training and employing a neural network architecture comprising a speaker verification neural network and a bandwidth expansion neural network. The bandwidth expansion neural network is trained on narrowband audio signals to produce and generate estimated wideband audio signals corresponding to the narrowband audio signals. These estimated wideband audio signals may be fed into one or more downstream applications, such as the speaker verification neural network or embedding extraction neural network. The speaker verification neural network can then compare and score inbound embeddings for a current call against enrolled embeddings, regardless of the channel used to receive the inbound signal or enrollment signal.

Abstract: Described herein are systems and methods for improved audio analysis using a computer-executed neural network having one or more in-network data augmentation layers. The systems described herein help ease or avoid unwanted strain on computing resources by employing the data augmentation techniques within the layers of the neural network. The in-network data augmentation layers will produce various types of simulated audio data when the computer applies the neural network on an inputted audio signal during a training phase, enrollment phase, and/or testing phase. Subsequent layers of the neural network (e.g., convolutional layer, pooling layer, data augmentation layer) ingest the simulated audio data and the inputted audio signal and perform various operations.

Abstract: A computer may segment a noisy audio signal into audio frames and execute a deep neural network (DNN) to estimate an instantaneous function of clean speech spectrum and noisy audio spectrum in the audio frame. This instantaneous function may correspond to a ratio of an a-priori signal to noise ratio (SNR) and an a-posteriori SNR of the audio frame. The computer may add estimated instantaneous function to the original noisy audio frame to output an enhanced speech audio frame.

Abstract: The present invention provides a method, a network device and a system for allowing for resuming a preceding incomplete synchronization session is provided, wherein the preceding incomplete synchronization session has been interrupted during its performing. In principle the resuming of the preceding incomplete synchronization session is based on the following operations according to the inventive concept. A communication connection for synchronization of data between a first and a second device is establishing. The first and the second device comprise each a predefined set of data records to be synchronized. A first and a second update identifier are communicated between the first and the second device. The first update identifier specifies a preceding complete synchronization session having been performed between them and the second update identifier specifies a preceding incomplete synchronization session having been performed between them.

Abstract: The present invention provides a method for generating a request for information about a data store. The request codes a data store descriptor which is used to characterize or identify a matching data store from a plurality of data stores. The re-transmitted information comprises retrieved information necessary for the requesting device or the applications executed thereon to access the identified data store, respectively. The present invention provides a method for generating a response in consequence on receiving a request for information a about data store. The data store descriptor coded in the request is used to characterize or identify a matching data store from a plurality of data stores and information about the data store is retrieved. This retrieved information coded in a response is transmitted to the previously requesting device enabling the requesting device or the applications executed thereon to access the identified data store, respectively.

Abstract: The present invention provides a method, a network device and a system for allowing for resuming a preceding incomplete synchronization session is provided, wherein the preceding incomplete synchronization session has been interrupted during its performing. In principle the resuming of the preceding incomplete synchronization session is based on the following operations according to the inventive concept. A communication connection for synchronization of data between a first and a second device is establishing. The first and the second device comprise each a predefined set of data records to be synchronized. A first and a second update identifier are communicated between the first and the second device. The first update identifier specifies a preceding complete synchronization session having been performed between them and the second update identifier specifies a preceding incomplete synchronization session having been performed between them.

Abstract: The present invention provides a method for generating a request for information about a data store. The request codes a data store descriptor which is used to characterize or identify a matching data store from a plurality of data stores. The re-transmitted information comprises retrieved information necessary for the requesting device or the applications executed thereon to access the identified data store, respectively. The present invention provides a method for generating a response in consequence on receiving a request for information a about data store. The data store descriptor coded in the request is used to characterize or identify a matching data store from a plurality of data stores and information about the data store is retrieved. This retrieved information coded in a response is transmitted to the previously requesting device enabling the requesting device or the applications executed thereon to access the identified data store, respectively.

Abstract: The present invention provides a method, a network device and a system for allowing for resuming a preceding incomplete synchronization session is provided, wherein the preceding incomplete synchronization session has been interrupted during its performing. In principle the resuming of the preceding incomplete synchronization session is based on the following operations according to the inventive concept. A communication connection for synchronization of data between a first and a second device is establishing. The first and the second device comprise each a predefined set of data records to be synchronized. A first and a second update identifier are communicated between the first and the second device. The first update identifier specifies a preceding complete synchronization session having been performed between them and the second update identifier specifies a preceding incomplete synchronization session having been performed between them.