Abstract: An audio processing device and method uses audio signals from a virtual rotating microphone for acoustic angle of arrival detection using a doppler effect technique.

Abstract: Techniques are provided herein for identifying the user of audio earbuds. In particular, a wearer's head filters an audio signal, and the audio filtering capabilities of a user's head are used as a biometric feature. One earbud can be used as an audio emitter and the other earbud as an audio receiver. A broadband sound can be generated by the speaker in one earbud and received at the microphone of the other earbud. The received sound is filtered by the user's head and the head characterization of the received filtered sound can be used to identify the user. In particular, the material properties of the user's head change the signal, such that the received signal at the microphone of the other earbud is different from the transmitted signal. The differences are unique to the user's head due to physiological variances among people, and can be used to identify the user.

Abstract: A system, article, device, apparatus, and method of audio processing comprises receiving, by processor circuitry, binaural audio signals at least overlapping at a same time and of a same two or more audio sources. The method also comprises generating localization map data indicating locations of the two or more audio sources relative to microphones providing the binaural audio signals and comprising inputting at least one version of the binaural audio signals into at least one neural network (NN).

Abstract: A system, article, device, apparatus, and method of binaural audio emulation comprises receiving, by processor circuitry, multiple audio signals from multiple microphones and overlapping in a same time and associated with a same at least one audio source. The method also comprises generating binaural audio signals comprising inputting at least one version of the multiple audio signals into a neural network.

Abstract: An apparatus, including: an interface configured to receive a target end-effector pose of a cobot; processing circuitry configured to: generate in a generic robot model a joint trajectory based on the target end-effector pose; employ a trained neural network model to map the joint trajectory generated in the generic robot model into a cobot model; and generate a movement instruction to control a movement of the cobot based on the joint trajectory mapped to the cobot model, wherein the generic robot model has a number of degrees of freedom that is equal to or greater than that of the cobot model.

Abstract: A self-reconfigurable controller for a robot, including: input/output (I/O) interfaces to enable communication with I/O peripheral devices coupled to the robot; and processing circuitry that is operable to: register the I/O peripheral devices and associated functionalities; receive a command for the robot to perform a task; conduct a self-awareness check to correlate functionalities to perform the task with functionalities of the I/O peripheral devices; and generate, based on a net of deep learning (DL) models and a result of the correlation, a target deep learning controller (TDLC) model to control the robot to perform the task.

Abstract: Methods and apparatuses for implementing a neural network using symmetric tensors. In embodiments, a system may include a higher order neural network with a plurality of layers that includes an input layer, one or more hidden layers, and an output layer. Each of the input layer, the one or more hidden layers, and the output layer includes a plurality of neurons, where the plurality of neurons includes at least first order neurons and second order neurons, and where inputs at a second order neuron are combined using a symmetric tensor.

Abstract: Systems and methods for audio source separation. A deep learning-based system uses an azimuth angle location to separate an audio signal originating from a selected location from other sound. Techniques are disclosed for steering a virtual direction of a microphone towards a selected speaker. A deep-learning based audio regression method, which can be implemented as a neural network, learns to separate out various speakers by leveraging spectral and spatial characteristics of all sources. The neural network can focus on multiple sources in multiple respective target directions, and cancel out other sounds. A user can choose which source to listen to. The network can use the time-domain signal and a frequency-domain signal to separate out the target signal and generate a separated audio output. The direction of the selected speaker relative to the microphone array can be input to the system as a vector.

Abstract: Techniques are provided herein for providing binaural sound signals that are virtually rotated to match head rotation, such that audio output to headphones is perceived to maintain its location relative to user when a user turns their head. In particular, techniques are presented to extract spherical location information already embedded in binaural signals to generate binaural sound signals that change to match head rotation. A deep-learning based audio regression method can use a 2-channel binaural audio signal and a rotation angle as input, and generate a new binaural audio output signal with the rotated environment corresponding to the rotation angle. The deep-learning based audio regression method can be implemented as a neural network, and can include deep learning operations, such as convolution, pooling, elementwise operation, linear operation, and nonlinear operation. A deep learning operation may be performed on internal parameters of the DNNs and one or more activations.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to enhance and audio signal.

Abstract: Methods, apparatus, systems, and articles of manufacture to detect the location of sound sources external to computing devices are disclosed. An apparatus, to determine a direction of a source of a sound relative to a computing device, includes a cross-correlation analyzer to generate a vector of values corresponding to a cross-correlation of first and second audio signals corresponding to the sound. The first audio signal is received from a first microphone of the computing device. The second audio signal is received from a second microphone of the computing device. The apparatus also includes a location analyzer to use a machine learning model and a set of the values of the vector to determine the direction of the source of the sound.

Abstract: Systems, apparatuses and methods may provide for technology that adjusts a plurality of weights in a neural network model and adjusts a plurality of activation functions in the neural network model. The technology may also output the neural network model in response to one or more conditions being satisfied by the plurality of weights and the plurality of activation functions. In one example, two or more of the activation functions are different from one another and the activation functions are adjusted on a per neuron basis.

Abstract: Hearing protection and communication apparatus using vibration sensors are disclosed. An example wearable electronic device includes means for transducing vibrations associated with speech into a first signal; means for transducing sound associated with ambient noise into a second signal; and means for processing to cause a speaker to output a signal to reduce the ambient noise; detect an identifier in the speech; and cause audio data representative of the speech to be transmitted to a second device associated with the identifier.

Abstract: A system is described herein. The system includes at least one hardware processor that is configured to identify a pre-determined acoustic barrier filter, wherein the acoustic barrier filter coincides with the physical acoustic barrier and receive an audio signal within a time window at the first microphone and the second microphone. The hardware processor is also configured to calculate a first measure of variability, a second measure of variability, a third measure of variability, and a fourth measure of variability. The hardware processor further concatenates the first measure of variability, the second measure of variability, the third measure of variability, and the fourth measure of variability to form a feature vector, and inputs the feature vector into a location classifier to obtain an audio source location.

Abstract: A method and apparatus to estimate and mitigate platform noise incurred at a wireless device coupled to the platform. A computing device includes a platform including a first processor, a wireless device coupled to the platform, the wireless device including a second processor, and a plurality of antennas coupled to the wireless device. At least one of the first processor or the second processor is configured to determine characteristics of a platform noise incurred at the wireless device in real-time due to utilization of circuitries and processing components on the platform and implement an adaptive real-time and application/context-aware measure to mitigate the platform noise based on the determined characteristics of the platform noise.

Abstract: A component of an Autonomous Vehicle (AV) system, the component having at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to decode data encoded in a signal, wherein the data identifies a pattern of protrusions embedded in a driving surface, the signal being received from at least one vehicle sensor resulting from a vehicle driving over the pattern of protrusions in the driving surface.

Abstract: A disclosed example includes providing vibration information to a model, the vibration information corresponding to a first vibration measured at a first mobile device when the first mobile device is in a state of non-use by a user, the model based on a plurality of vibration patterns that correspond to second vibrations measured by second mobile devices in different environments; identifying, using the model, one of the vibration patterns that corresponds to the vibration information; determining an environment of the first mobile device based on the one of the vibration patterns; and instructing the first mobile device to modify a functionality of the first mobile device based on the environment.

Abstract: This disclosure describes systems, methods, and devices related to reducing noise in and improving speech signals and radio signals. A device may identify a radio frequency signal received by radio frequency circuity at a time; apply a Short Time Fourier Transform (STFT) to the radio frequency signal to generate a STFT signal; identify a signal-to-noise ratio associated with the radio frequency signal at the time; identify a packet-error-rate associated with the radio frequency signal at the time; identify activity when the multiplexed radio frequency signal is received by the radio frequency circuity at the time; select, based on the signal-to-noise ratio, the packet-error-rate, and the activity, a spectral mask to be applied to the STFT signal; apply the selected spectral mask to the STFT signal to generate a clean STFT signal; and apply an inverse STFT to the clean STFT signal to generate a clean radio frequency signal.

Abstract: Techniques are disclosed to use existing vehicle speakers alone or in conjunction with other sensors (e.g. SRS sensors and/or microphones) that may already be implemented as part of the vehicle to identify acoustic signatures. Suitable low-cost and widely available hardware components (e.g., relays) may be used to modify the vehicle's existing speakers for a bi-directional mode of operation. Moreover, the vehicle's existing of audio amplifiers may be used to amplify signals collected by the speakers when operating in “reverse,” and process these collected signals to determine vehicle state information.

Abstract: This disclosure describes systems, methods, and devices related to presenting video conferencing virtual seating arrangements. A method may include generating a first similarity score indicative of a first similarity between a first voice of a first virtual meeting user and a second voice of a second virtual meeting user; generating a second similarity score indicative of a second similarity between the first voice of the first virtual meeting user and a third voice of a third virtual meeting user; determining, based on the first similarity score and the second similarity score, a similarity loss for a virtual seating arrangement; determining that the similarity loss is a minimum similarity loss of respective similarity losses for different virtual seating arrangements; generating presentation data, for the virtual meeting, including virtual representations of the virtual meeting users arranged based on the virtual seating arrangement; and presenting the presentation data.