Abstract: A method performed by an electronic device in a room. The method performs an enrollment process in which a spatial profile of a location of an artificial sound source is created and performs an identification process that determines whether a sound event within the room is produced by the artificial sound source by 1) capturing the sound event using a microphone array and 2) determining a likelihood that the sound event occurred at the location of the artificial sound source.

Abstract: An electronic device includes a processor, and a memory containing instructions that, when executed by the processor, cause the electronic device to learn a sound emitted by a legacy device and to issue an output when the electronic device subsequently hears the sound. For example, the electronic device can receive a training input and extract a compact representation of a sound in the training input, which the device stores. The device can receive an audio signal corresponding to an observed acoustic scene and extract a representation of the observed acoustic scene from the audio signal. The electronic device can determine whether the sound is present in the observed acoustic scene at least in part from a comparison of the representation of the observed acoustic scene with the representation of the sound. The electronic device emits a selected output responsive to determining that the sound is present in the acoustic scene.

Abstract: Implementations of the subject technology provide systems and methods for providing audio source separation for audio input, such as for audio devices having limited power and/or computing resources. The subject technology may allow an audio device to leverage processing and/or power resources of a companion device that is communicatively coupled to the audio device. The companion device may identify a noise condition of the audio device, select a source separation model based on the noise condition, and provide the source separation model to the audio device. In this way, the audio device can provide audio source separation functionality using a relatively small footprint source separation model that is specific to the noise condition in which the audio device is operated.

Abstract: Microphone signals of a primary headphone are processed and either a first transparency mode of operation is activated or a second transparency mode of operation. In another aspect, a processor enters different configurations in response to estimated ambient acoustic noise being lower or higher than a threshold, wherein in a first configuration a transparency audio signal is adapted via target voice and wearer voice processing (TVWVP) of a microphone signal to boost detected speech frequencies in the transparency audio signal, and in a second configuration the TVWVP is controlled to, as the estimated ambient acoustic noise increases, reduce boosting of, or not boost at all, the detected speech frequencies in the transparency audio signal. Other aspects are also described and claimed.

Abstract: Microphone signals of a primary headphone are processed and either a first transparency mode of operation is activated or a second transparency mode of operation. In another aspect, a processor enters different configurations in response to estimated ambient acoustic noise being lower or higher than a threshold, wherein in a first configuration a transparency audio signal is adapted via target voice and wearer voice processing (TVWVP) of a microphone signal to boost detected speech frequencies in the transparency audio signal, and in a second configuration the TVWVP is controlled to, as the estimated ambient acoustic noise increases, reduce boosting of, or not boost at all, the detected speech frequencies in the transparency audio signal. Other aspects are also described and claimed.

Abstract: A method performed by an electronic device in a room. The method performs an enrollment process in which a spatial profile of a location of an artificial sound source is created and performs an identification process that determines whether a sound event within the room is produced by the artificial sound source by 1) capturing the sound event using a microphone array and 2) determining a likelihood that the sound event occurred at the location of the artificial sound source.

Abstract: The subject disclosure provides systems and methods for generating and storing learned embeddings of audio inputs to an electronic device. The electronic device may generate and store encoded versions of audio inputs and learned embeddings of the audio inputs. When a new audio input is obtained, the electronic device can generate an encoded version of the new audio input, compare the encoded version of the new audio input to the stored encoded versions of prior audio inputs, and if the encoded version of the new audio input matches one of the stored encoded versions of the prior audio inputs, the electronic device can provide a stored learned embedding that corresponds to the one of the stored encoded versions of the prior audio inputs to a detection model at the electronic device. The cached embeddings can be provided to locally trained models for detecting individual sounds using electronic devices.

Abstract: Implementations of the subject technology provide systems and methods for providing audio source separation for audio input, such as for audio devices having limited power and/or computing resources. The subject technology may allow an audio device to leverage processing and/or power resources of a companion device that is communicatively coupled to the audio device. The companion device may identify a noise condition of the audio device, select a source separation model based on the noise condition, and provide the source separation model to the audio device. In this way, the audio device can provide audio source separation functionality using a relatively small footprint source separation model that is specific to the noise condition in which the audio device is operated.

Abstract: An electronic device has one or more microphones that pick up a sound. At least one feature extractor processes the audio signals from the microphones, that contain the picked up the sound, to determine several features for the sound. The electronic device also includes a classifier that has a machine learning model which is configured to determine a sound classification, such as artificial versus natural for the sound, based upon at least one of the determined features. Other aspects are also described and claimed.

Abstract: An electronic device includes a processor, and a memory containing instructions that, when executed by the processor, cause the electronic device to learn a sound emitted by a legacy device and to issue an output when the electronic device subsequently hears the sound. For example, the electronic device can receive a training input and extract a compact representation of a sound in the training input, which the device stores. The device can receive an audio signal corresponding to an observed acoustic scene and extract a representation of the observed acoustic scene from the audio signal. The electronic device can determine whether the sound is present in the observed acoustic scene at least in part from a comparison of the representation of the observed acoustic scene with the representation of the sound. The electronic device emits a selected output responsive to determining that the sound is present in the acoustic scene.

Abstract: An electronic device includes a processor, and a memory containing instructions that, when executed by the processor, cause the electronic device to learn a sound emitted by a legacy device and to issue an output when the electronic device subsequently hears the sound. For example, the electronic device can receive a training input and extract a compact representation of a sound in the training input, which the device stores. The device can receive an audio signal corresponding to an observed acoustic scene and extract a representation of the observed acoustic scene from the audio signal. The electronic device can determine whether the sound is present in the observed acoustic scene at least in part from a comparison of the representation of the observed acoustic scene with the representation of the sound. The electronic device emits a selected output responsive to determining that the sound is present in the acoustic scene.

Abstract: A method performed by an electronic device in a room. The method performs an enrollment process in which a spatial profile of a location of an artificial sound source is created and performs an identification process that determines whether a sound event within the room is produced by the artificial sound source by 1) capturing the sound event using a microphone array and 2) determining a likelihood that the sound event occurred at the location of the artificial sound source.

Abstract: The subject disclosure provides systems and methods for providing locally trained models for detecting individual sounds using electronic devices. Local detection of individual sounds with a detection model at an electronic device can be provided by obtaining training samples for the detection model with the electronic device, and generating additional negative and positive training samples based on the obtained training samples. A two-stage detection process may be provided, in which a trigger model at a device compares an audio input to a reference sound to trigger a detection model at the device. The detection of individual sounds with a detection model at an electronic device can also leverage audio capture capabilities of multiple devices in an acoustic scene to capture multiple concurrent training samples.

Abstract: An electronic device has one or more microphones that pick up a sound. At least one feature extractor processes the audio signals from the microphones, that contain the picked up the sound, to determine several features for the sound. The electronic device also includes a classifier that has a machine learning model which is configured to determine a sound classification, such as artificial versus natural for the sound, based upon at least one of the determined features. Other aspects are also described and claimed.

Abstract: A method performed by an electronic device in a room. The method performs an enrollment process in which a spatial profile of a location of an artificial sound source is created and performs an identification process that determines whether a sound event within the room is produced by the artificial sound source by 1) capturing the sound event using a microphone array and 2) determining a likelihood that the sound event occurred at the location of the artificial sound source.

Abstract: Implementations of the subject technology provide systems and methods for providing audio source separation for audio input, such as for audio devices having limited power and/or computing resources. The subject technology may allow an audio device to leverage processing and/or power resources of a companion device that is communicatively coupled to the audio device. The companion device may identify a noise condition of the audio device, select a source separation model based on the noise condition, and provide the source separation model to the audio device. In this way, the audio device can provide audio source separation functionality using a relatively small footprint source separation model that is specific to the noise condition in which the audio device is operated.

Abstract: An electronic device includes a processor, and a memory containing instructions that, when executed by the processor, cause the electronic device to learn a sound emitted by a legacy device and to issue an output when the electronic device subsequently hears the sound. For example, the electronic device can receive a training input and extract a compact representation of a sound in the training input, which the device stores. The device can receive an audio signal corresponding to an observed acoustic scene and extract a representation of the observed acoustic scene from the audio signal. The electronic device can determine whether the sound is present in the observed acoustic scene at least in part from a comparison of the representation of the observed acoustic scene with the representation of the sound. The electronic device emits a selected output responsive to determining that the sound is present in the acoustic scene.

Abstract: Embodiments of the present disclosure can provide systems, methods, and computer-readable medium for providing audio and/or video effects based at least in part on facial features and/or voice feature characteristics of the user. For example, video and/or an audio signal of the user may be recorded by a device. Voice audio features and facial feature characteristics may be extracted from the voice audio signal and the video, respectively. The facial features of the user may be used to modify features of a virtual avatar to emulate the facial feature characteristics of the user. The extracted voice audio features may modified to generate an adjusted audio signal or an audio signal may be composed from the voice audio features. The adjusted/composed audio signal may simulate the voice of the virtual avatar. A preview of the modified video/audio may be provided at the user's device.

Abstract: An electronic device has one or more microphones that pick up a sound. At least one feature extractor processes the audio signals from the microphones, that contain the picked up the sound, to determine several features for the sound. The electronic device also includes a classifier that has a machine learning model which is configured to determine a sound classification, such as artificial versus natural for the sound, based upon at least one of the determined features. Other aspects are also described and claimed.

Abstract: Embodiments of the present disclosure can provide systems, methods, and computer-readable medium for providing audio and/or video effects based at least in part on facial features and/or voice feature characteristics of the user. For example, video and/or an audio signal of the user may be recorded by a device. Voice audio features and facial feature characteristics may be extracted from the voice audio signal and the video, respectively. The facial features of the user may be used to modify features of a virtual avatar to emulate the facial feature characteristics of the user. The extracted voice audio features may modified to generate an adjusted audio signal or an audio signal may be composed from the voice audio features. The adjusted/composed audio signal may simulate the voice of the virtual avatar. A preview of the modified video/audio may be provided at the user's device.