A method, computer program product, and computing system for receiving a signal from each microphone of a plurality of microphones, thus defining a plurality of signals. One or more inter-microphone gain-based augmentations may be performed on the plurality of signals, thus defining one or more inter-microphone gain-augmented signals.

A method, computer program product, and computing system for receiving a signal from each microphone of a plurality of microphones, thus defining a plurality of signals. One or more inter-microphone gain-based augmentations may be performed on the plurality of signals, thus defining one or more inter-microphone gain-augmented signals.

Systems and methods for optimizing network microphone devices using noise classification are disclosed herein. In one example, individual microphones of a network microphone device (NMD) detect sound. The sound data is analyzed to detect a trigger event such as a wake word. Metadata associated with the sound data is captured in a lookback buffer of the NMD. After detecting the trigger event, the metadata is analyzed to classify noise in the sound data. Based on the classified noise, at least one performance parameter of the NMD is modified.

Systems and methods for optimizing network microphone devices using noise classification are disclosed herein. In one example, individual microphones of a network microphone device (NMD) detect sound. The sound data is analyzed to detect a trigger event such as a wake word. Metadata associated with the sound data is captured in a lookback buffer of the NMD. After detecting the trigger event, the metadata is analyzed to classify noise in the sound data. Based on the classified noise, at least one performance parameter of the NMD is modified.

Method includes receiving, at a server device, from a plurality of input devices, audio data. The audio data of each input device corresponds to a time-related portion of the audio data. The method determines a speech energy level for each input device by providing the time-related audio portion as input to a trained model. For each input device, a statistical value associated with the speech energy level is determined. A strongest input device is identified based on the statistical value. The statistical value associated with the speech energy level of each input device other than the strongest input device is compared to the statistical value of the strongest input device. Depending on the comparison, the method determines whether to update the gain value of an input device to an estimated target gain value based on the statistical value of the speech energy level of the respective input device.

Method includes receiving, at a server device, from a plurality of input devices, audio data. The audio data of each input device corresponds to a time-related portion of the audio data. The method determines a speech energy level for each input device by providing the time-related audio portion as input to a trained model. For each input device, a statistical value associated with the speech energy level is determined. A strongest input device is identified based on the statistical value. The statistical value associated with the speech energy level of each input device other than the strongest input device is compared to the statistical value of the strongest input device. Depending on the comparison, the method determines whether to update the gain value of an input device to an estimated target gain value based on the statistical value of the speech energy level of the respective input device.

Disclosed herein, among other things, are systems and methods for a user adjustment interface using remote computing resources. Specifically, a system can include a mobile device in communication with a hearing assistance device or a remote server. The mobile device can interpret an acoustic environment and send information about the environment to a remote server. The remote server can determine and send information to the mobile device for use in a user interface. The mobile device can receive a user selection of hearing assistance parameter information to be sent to the hearing assistance device.

Disclosed herein, among other things, are systems and methods for a user adjustment interface using remote computing resources. Specifically, a system can include a mobile device in communication with a hearing assistance device or a remote server. The mobile device can interpret an acoustic environment and send information about the environment to a remote server. The remote server can determine and send information to the mobile device for use in a user interface. The mobile device can receive a user selection of hearing assistance parameter information to be sent to the hearing assistance device.

Two-stage speech/music classification device and method classify an input sound signal and select a core encoder for encoding the sound signal. A first stage classifies the input sound signal into one of a number of final classes. A second stage extracts high-level features of the input sound signal and selects the core encoder for encoding the input sound signal in response to the extracted high-level features and the final class selected in the first stage.

Two-stage speech/music classification device and method classify an input sound signal and select a core encoder for encoding the sound signal. A first stage classifies the input sound signal into one of a number of final classes. A second stage extracts high-level features of the input sound signal and selects the core encoder for encoding the input sound signal in response to the extracted high-level features and the final class selected in the first stage.