Abstract: A method for auralizing a multi-microphone device. Path information for one or more sound paths using dimensions and room reflection coefficients of a simulated room for one of a plurality of microphones included in a multi-microphone device is determined. An array-related transfer functions (ARTFs) for the one of the plurality of microphones is retrieved. The auralized impulse response for the one of the plurality of microphones is generated based at least on the retrieved ARTFs and the determined path information.

Abstract: The technology generally relates to spatial audio communication between devices. For example, a first device and a second device may be connected via a communication link. The first device may capture audio signals in an environment through two or more microphones. The first device may encode the captured audio with spatial configuration data. The first device may transmit the encoded audio via the communication link to the second device. The second device may decode the encoded audio into binaural or ambisonic audio to be output by one or more speakers of the second device. The binaural or ambisonic audio may be converted into spatial audio to be output. The second device may output the binaural or spatial audio to create an immersive listening experience.

Abstract: A method for auralizing a multi-microphone device. Path information for one or more sound paths using dimensions and room reflection coefficients of a simulated room for one of a plurality of microphones included in a multi-microphone device is determined. An array-related transfer functions (ARTFs) for the one of the plurality of microphones is retrieved. The auralized impulse response for the one of the plurality of microphones is generated based at least on the retrieved ARTFs and the determined path information.

Abstract: A method for auralizing a multi-microphone device. Path information for one or more sound paths using dimensions and room reflection coefficients of a simulated room for one of a plurality of microphones included in a multi-microphone device is determined. An array-related transfer functions (ARTFs) for the one of the plurality of microphones is retrieved. The auralized impulse response for the one of the plurality of microphones is generated based at least on the retrieved ARTFs and the determined path information.

Abstract: A method of presenting appropriate actions for responding to a visitor to a smart home environment via an electronic greeting system of the smart home environment, including detecting a visitor of the smart home environment; obtaining context information from the smart home environment regarding the visitor; based on the context information, identifying a plurality of appropriate actions available to a user of a client device for interacting with the visitor via the electronic greeting system; and causing the identified actions to be presented to the user of the client device.

Abstract: Systems and methods are described for improving endpoint detection of a voice query submitted by a user. In some implementations, a synchronized video data and audio data is received. A sequence of frames of the video data that includes images corresponding to lip movement on a face is determined. The audio data is endpointed based on first audio data that corresponds to a first frame of the sequence of frames and second audio data that corresponds to a last frame of the sequence of frames. A transcription of the endpointed audio data is generated by an automated speech recognizer. The generated transcription is then provided for output.

Abstract: A method of presenting appropriate actions for responding to a visitor to a smart home environment via an electronic greeting system of the smart home environment, including detecting a visitor of the smart home environment; obtaining context information from the smart home environment regarding the visitor; based on the context information, identifying a plurality of appropriate actions available to a user of a client device for interacting with the visitor via the electronic greeting system; and causing the identified actions to be presented to the user of the client device.

Abstract: Systems and techniques are provided for sound model localization within an environment. Sound recordings of sounds in the environment may be received from devices in the environment. Preliminary labels for the sound recordings may be determined using pre-trained sound models. The preliminary labels may have associated probabilities. Sound clips with preliminary labels may be generated based on sound recordings that have preliminary labels whose probability is over a high-recall threshold for the pre-trained sound model that determined the preliminary label. The sound clips with preliminary labels may be sent to a user device. Labeled sound clips may be received from the user device. The labeled sound clips may be based on the sound clips with preliminary labels. Training data sets may be generated for the pre-trained sound models using the labeled sound clips. The pre-trained sound models may be trained using the training data sets to generate localized sound models.

Abstract: A method of presenting appropriate actions for responding to a visitor to a smart home environment via an electronic greeting system of the smart home environment, including detecting a visitor of the smart home environment; obtaining context information from the smart home environment regarding the visitor; based on the context information, identifying a plurality of appropriate actions available to a user of a client device for interacting with the visitor via the electronic greeting system; and causing the identified actions to be presented to the user of the client device.

Abstract: Systems and methods are described for improving endpoint detection of a voice query submitted by a user. In some implementations, a synchronized video data and audio data is received. A sequence of frames of the video data that includes images corresponding to lip movement on a face is determined. The audio data is endpointed based on first audio data that corresponds to a first frame of the sequence of frames and second audio data that corresponds to a last frame of the sequence of frames. A transcription of the endpointed audio data is generated by an automated speech recognizer. The generated transcription is then provided for output.

Abstract: This application discloses a method implemented by an electronic device to detect a signature event (e.g., a baby cry event) associated with an audio feature (e.g., baby sound). The electronic device obtains a classifier model from a remote server. The classifier model is determined according to predetermined capabilities of the electronic device and ambient sound characteristics of the electronic device, and distinguishes the audio feature from a plurality of alternative features and ambient noises. When the electronic device obtains audio data, it splits the audio data to a plurality of sound components each associated with a respective frequency or frequency band and including a series of time windows. The electronic device further extracts a feature vector from the sound components, classifies the extracted feature vector to obtain a probability value according to the classifier model, and detects the signature event based on the probability value.

Abstract: A system is described that constantly learns the sound characteristics of an indoor environment to detect the presence or absence of humans within that environment. A detection model is constructed and a decision feedback approach is used to constantly learn and update the statistics of the detection features and sound events that are unique to the environment in question. The learning process may not only rely on acoustic signal, but may also make use of signals derived from other sensors such as range sensor, motion sensors, pressure sensors, and video sensors.

Abstract: A method for auralizing a multi-microphone device. Path information for one or more sound paths using dimensions and room reflection coefficients of a simulated room for one of a plurality of microphones included in a multi-microphone device is determined. An array-related transfer functions (ARTFs) for the one of the plurality of microphones is retrieved. The auralized impulse response for the one of the plurality of microphones is generated based at least on the retrieved ARTFs and the determined path information.

Abstract: A sensor device may include a computing device in communication with multiple microphones. A neural network executing on the computing device may receive audio signals from each microphone. One microphone signal may serve as a reference signal. The neural network may extract differences in signal characteristics of the other microphone signals as compared to the reference signal. The neural network may combine these signal differences into a lossy compressed signal. The sensor device may transmit the lossy compressed signal and the lossless reference signal to a remote neural network executing in a cloud computing environment for decompression and sound recognition analysis.

Abstract: Systems and methods are described for improving endpoint detection of a voice query submitted by a user. In some implementations, a synchronized video data and audio data is received. A sequence of frames of the video data that includes images corresponding to lip movement on a face is determined. The audio data is endpointed based on first audio data that corresponds to a first frame of the sequence of frames and second audio data that corresponds to a last frame of the sequence of frames. A transcription of the endpointed audio data is generated by an automated speech recognizer. The generated transcription is then provided for output.

Abstract: A system is described that constantly learns the sound characteristics of an indoor environment to detect the presence or absence of humans within that environment. A detection model is constructed and a decision feedback approach is used to constantly learn and update the statistics of the detection features and sound events that are unique to the environment in question. The learning process may not only rely on acoustic signal, but may also make use of signals derived from other sensors such as range sensor, motion sensors, pressure sensors, and video sensors.

Abstract: A method for auralizing a multi-microphone device. Path information for one or more sound paths using dimensions and room reflection coefficients of a simulated room for one of a plurality of microphones included in a multi-microphone device is determined. An array-related transfer functions (ARTFs) for the one of the plurality of microphones is retrieved. The auralized impulse response for the one of the plurality of microphones is generated based at least on the retrieved ARTFs and the determined path information.

Abstract: Systems and methods of a security system are provided, including detecting, by a sensor, a sound event, and selecting, by a processor coupled to the sensor, at least a portion of sound data captured by the sensor that corresponds to at least one sound feature of the detected sound event. The systems and methods include classifying the at least one sound feature into one or more sound categories, and determining, by a processor, based upon a database of home-specific sound data, whether the at least one sound feature is a human-generated sound. A notification can be transmitted to a computing device according to the sound event.

Abstract: A system is described that constantly learns the sound characteristics of an indoor environment to detect the presence or absence of humans within that environment. A detection model is constructed and a decision feedback approach is used to constantly learn and update the statistics of the detection features and sound events that are unique to the environment in question. The learning process may not only rely on acoustic signal, but may also make use of signals derived from other sensors such as range sensor, motion sensors, pressure sensors, and video sensors.

Abstract: An example method includes receiving, by a computing system, an indication of one or more audible sounds that are detected by a first sensing device, the one or more audible sounds originating from a user; determining, by the computing system and based at least in part on an indication of one or more signals detected by a second sensing device, a distance between the user and the second sensing device; determining, by the computing system and based at least in part on the indication of the one or more audible sounds, one or more acoustic features that are associated with the one or more audible sounds; and determining, by the computing system, and based at least in part on the one or more acoustic features and the distance between the user and the second sensing device, one or more words that correspond to the audible sounds.