Abstract:
Use of spoken input for user devices, e.g. smartphones, can be challenging due to presence of other sound sources. Blind source separation (BSS) techniques aim to separate a sound generated by a particular source of interest from a mixture of different sounds. Various BSS techniques disclosed herein are based on recognition that providing additional information that is considered within iterations of a nonnegative tensor factorization (NTF) model improves accuracy and efficiency of source separation. Examples of such information include direction estimates or neural network models trained to recognize a particular sound of interest. Furthermore, identifying and processing incremental changes to an NTF model, rather than re-processing the entire model each time data changes, provides an efficient and fast manner for performing source separation on large sets of quickly changing data. Carrying out at least parts of BSS techniques in a cloud allows flexible utilization of local and remote sources.
Abstract:
The disclosed apparatus and methods include a reconfigurable sampling accelerator and a method of using the reconfigurable sampling accelerator, respectively. The reconfigurable sampling accelerator can be adapted to a variety of target applications. The reconfigurable sampling accelerator can include a sampling module, a memory system, and a controller that is configured to coordinate operations in the sampling module and the memory system. The sampling module can include a plurality of sampling units, and the plurality of sampling units can be configured to generate samples in parallel. The sampling module can leverage inherent characteristics of a probabilistic model to generate samples in parallel.
Abstract:
The present disclosure relates generally to improving audio processing using an intelligent microphone and, more particularly, to techniques for processing audio received at a microphone with integrated analog-to-digital conversion, digital signal processing, acoustic source separation, and for further processing by a speech recognition system. Embodiments of the present disclosure include intelligent microphone systems designed to collect and process high-quality audio input efficiently. Systems and method for audio processing using an intelligent microphone include an integrated package with one or more microphones, analog-to-digital converters (ADCs), digital signal processors (DSPs), source separation modules, memory, and automatic speech recognition. Systems and methods are also provided for audio processing using an intelligent microphone that includes a microphone array and uses a preprogrammed audio beamformer calibrated to the included microphone array.
Abstract:
The present disclosure relates generally to improving acoustic source tracking and selection and, more particularly, to techniques for acoustic source tracking and selection using motion or position information. Embodiments of the present disclosure include systems designed to select and track acoustic sources. In one embodiment, the system may be realized as an integrated circuit including a microphone array, motion sensing circuitry, position sensing circuitry, analog-to-digital converter (ADC) circuitry configured to convert analog audio signals from the microphone array into digital audio signals for further processing, and a digital signal processor (DSP) or other circuitry for processing the digital audio signals based on motion data and other sensor data. Sensor data may be correlated to the analog or digital audio signals to improve source separation or other audio processing.
Abstract:
A system is provided for updated processing of audio signals in a vehicle. The system includes a microphone, a transceiver and head unit. The microphone receives audio signals. The transceiver sends the received audio signals to a cloud computing system for processing, and receives the processed audio signals from the cloud computing system. The head unit receives the processed audio signals from the transceiver and plays the processed audio data through the vehicle's audio system.
Abstract:
Heart rate monitors are plagued by noisy photoplethysmography (PPG) data, which makes it difficult for the monitors to output a consistently accurate heart rate reading. Noise is often caused by motion. Using known methods for processing accelerometer readings that measure movement to filter out some of this noise may help, but not always. The present disclosure describes an improved filtering approach, referred to herein as an iterative frequency-domain mask estimation technique, based on using frequency-domain representation (e.g. STFT) of PPG data and accelerometer data for each accelerometer channel to generate filters for filtering the PPG signal from motion-related artifacts prior to tracking frequency of the heartbeat (heart rate). Implementing this technique leads to more accurate heart rate measurements.