Sonos, Inc.

Charles Conor Sleith

Kurt Thomas Soto

Connor Kristopher Smith

John G Tolomei

Patrick Kevin McPherson

Roberto Maria Dizon

Patrick Kevin Mcpherson

Systems and methods for determining and adapting to changes in microphone performance of playback devices are disclosed herein. In one example, an audio input is received at an array of individual microphones of a network microphone device. Output microphone signals are generated from each of the individual microphones based on the audio input. The output microphone signals are analyzed to detect a trigger event. After detecting the trigger event, the output microphone signals are compared to detect aberrant behavior of one or more of the microphones. Optionally, corrective actions can be taken or suggested based on the detection of aberrant behavior of one or more microphones.

Determining and adapting to changes in microphone performance of playback devices

Systems and methods for generating training data are described herein. Pieces of metadata captured by a plurality of networked sensor systems can be captured, where each piece of metadata is associated with a specific set of sensor data captured by one of the plurality of networked sensor systems and includes a set of characteristics for the specific set of captured sensor data. A probabilistic model can be generated based on the received metadata and simulations can be performed based upon a training corpus by generating multiple scenarios, and, for each scenario, a scenario specific version of a particular annotated sample is generated by performing a simulation using the particular annotated sample. The scenario specific versions of annotated samples from the training corpus can be stored as a training data set on the at least one network device.

Systems and methods for generating labeled data to facilitate configuration of network microphone devices

Systems and methods for optimizing voice detection via a network microphone device are disclosed herein. In one example, individual microphones of a network microphone device detect sound. The sound data is captured in a first buffer and analyzed to detect a trigger event. Metadata associated with the sound data is captured in a second buffer and provided to at least one network device to determine at least one characteristic of the detected sound based on the metadata. The network device provides a response that includes an instruction, based on the determined characteristic, to modify at least one performance parameter of the NMD. The NMD then modifies the at least one performance parameter based on the instruction.

Voice detection optimization using sound metadata

Systems and methods for localizing portable devices are illustrated. One embodiment includes a method for locating a portable device in a network that includes several reference devices. The method measures characteristics of signals transmitted via signal paths between reference devices and a portable device, normalizes the measurements to estimate characteristics of the signal paths, and estimates the likelihood that the portable device is in a particular location. Systems and methods for training prediction models include a method that includes steps for receiving context data for a portable device in a system, wherein the context data includes localization data that describes a location of the portable device, identifying a predicted stationary device based on the context data using a prediction model, identifying a target stationary device from the several stationary devices, training the prediction model based on based on the predicted stationary device and the received input.

Systems and methods for device localization

In one aspect, a playback device is provided that is configured to identify a trigger event indicating a request to associate the playback device with another playback device. Based on identifying the trigger event, the playback device is configured to create a first sound code based on a first sound specimen detected by the playback device. After identifying the trigger event, the playback device is configured to receive from the other playback device a sound object and based on receiving the sound object, identify a second sound code. The playback device is also configured to, based on the first sound code and the second sound code, determine that it and the other playback device have a spatial relationship. Based on that determination, the playback device is configured to cause it and the other playback device to be associated in accordance with the indicated request.

Networked devices, systems, and methods for associating playback devices based on sound codes

Systems and methods for optimizing voice detection via a network microphone device (NMD) based on a selected voice-assistant service (VAS) are disclosed herein. In one example, the NMD detects sound via individual microphones and selects a first VAS to communicate with the NMD. The NMD produces a first sound-data stream based on the detected sound using a spatial processor in a first configuration. Once the NMD determines that a second VAS is to be selected over the first VAS, the spatial processor assumes a second configuration for producing a second sound-data stream based on the detected sound. The second sound-data stream is then transmitted to one or more remote computing devices associated with the second VAS.

Voice detection optimization based on selected voice assistant service

Systems and methods for training prediction models are illustrated. One embodiment includes a method for training a prediction model in a network. The method includes steps for receiving context data for a portable device in a system, wherein the context data includes localization data that describes a location of the portable device, identifying a predicted stationary device from several stationary devices that is predicted based on the context data using a prediction model, receiving input identifying a target stationary device from the several stationary devices, generating training data based on the predicted stationary device and the received input, updating the prediction model based on the generated training data.

Systems and methods for target device prediction

In one aspect, a playback deice is configured to identify in an audio stream, via a second wake-word engine, a false wake word for a first wake-word engine that is configured to receive as input sound data based on sound detected by a microphone. The first and second wake-word engines are configured according to different sensitivity levels for false positives of a particular wake word. Based on identifying the false wake word, the playback device is configured to (i) deactivate the first wake-word engine and (ii) cause at least one network microphone device to deactivate a wake-word engine for a particular amount of time. While the first wake-word engine is deactivated, the playback device is configured to cause at least one speaker to output audio based on the audio stream. After a predetermined amount of time has elapsed, the playback device is configured to reactivate the first wake-word engine.

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Waltham, MA, United States of America

Charles Conor Sleith