Abstract: An endpoint selectively enhances a captured audio signal based on an operating mode. The endpoint obtains an audio input signal of multiple users in a physical location. The audio input signal is captured by a microphone. The endpoint separates voice signals from the audio input signal and determines an operating mode for an audio output signal. The endpoint selectively adjusts each of the voice signals based on the operating mode to generate the audio output signal.

Abstract: Some embodiments perform, in a multi-layer neural network in a computing device, a convolution operation on input feature maps with multiple convolutional filters. The convolutional filters have multiple filter precisions. In other embodiments, electronic design automation (EDA) systems, methods, and computer-readable media are presented for adding such a multi-layer neural network into an integrated circuit (IC) design.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Some embodiments perform, in a multi-layer neural network in a computing device, optimization of the multi-layer neural network, for example by making a convolutional change with a first plurality of convolutional filters, or by making a connection change of a first plurality of convolutional filters. In other embodiments, electronic design automation (EDA) systems, methods, and computer-readable media are presented for adding such a multi-layer neural network into an integrated circuit (IC) design.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: The present disclosure relates to a computer-implemented method for analyzing one or more hyper-parameters for a multi-layer computational structure. The method may include accessing, using at least one processor, input data for recognition. The input data may include at least one of an image, a pattern, a speech input, a natural language input, a video input, and a complex data set. The method may further include processing the input data using one or more layers of the multi-layer computational structure and performing matrix factorization of the one or more layers. The method may also include analyzing one or more hyper-parameters for the one or more layers based upon, at least in part, the matrix factorization of the one or more layers.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Aspects of the present disclosure involve a transform domain regression convolutional neural network for image segmentation. Example embodiments include a system comprising a machine-readable storage medium storing instructions and computer-implemented methods for classifying one or more pixels in an image. The method may include analyzing the image to estimate one or more transform domain coefficients using a multi-layered function such as a convolutional neural network. The method may further include generating a segmented image by applying a change of basis transformation to the estimated one or more transform domain coefficients.

Abstract: A method allocates bandwidth from a radio frequency spectrum in a cellular network including a set of cells. Each cell includes a base station for serving a set of mobile stations in the cell. An area around each base station is partitioned into a center region and an edge region. In each base station, cell-center bandwidth for use by the mobile stations in the center region is reserved according to an inter-cell interference coordination (ICIC) protocol, and cell-edge bandwidth for use by the mobile stations in the edge region is reserved according to the ICIC protocol. The bandwidth can be fixed or adaptive to reduce the signaling overhead. The adaptive bandwidth can be further partitioned into reserved and the free bands. Mobile stations are classified as primary and secondary users, depending on whether they use or are assigned the fixed or adaptive band radio resources.

Abstract: A method allocates bandwidth from a radio frequency spectrum in a cellular network including a set of cells. Each cell includes a base station for serving a set of mobile stations in the cell. An area around each base station is partitioned into a center region and a boundary region. In each base station, bandwidth for use in the center region is reserved according to an inter-cell interference coordination (ICIC) protocol, and bandwidth for use in the boundary region is reserved according to the ICIC protocol and a base station cooperation (BSC) protocol. Then, the bandwidth is allocated to mobile stations as the mobile stations communicate with the base station in the center regions and the boundary regions according to the bandwidth reservations.

Abstract: A method allocates bandwidth from a radio frequency spectrum in a cellular network including a set of cells. Each cell includes a base station for serving a set of mobile stations in the cell. An area around each base station is partitioned into a center region and an edge region. In each base station, cell-center bandwidth for use by the mobile stations in the center region is reserved according to an inter-cell interference coordination (ICIC) protocol, and cell-edge bandwidth for use by the mobile stations in the edge region is reserved according to the ICIC protocol. The bandwidth can be fixed or adaptive to reduce the signaling overhead. The adaptive bandwidth can be further partitioned into reserved and the free bands. Mobile stations are classified as primary and secondary users, depending on whether they use or are assigned the fixed or adaptive band radio resources.