Abstract: Training a user-specific perturbation generator for an audio feature detection model includes receiving one or more positive audio samples of a user, each of the one or more positive audio samples including an audio feature; receiving one or more negative audio samples of the user, each of the one or more negative audio samples sharing an acoustic similarity with at least one of the one or more positive audio samples; and adversarially training a user-specific perturbation generator model to generate a user-specific perturbation, the training based on the one or more positive audio samples and the one or more negative audio samples. Perturbing audio samples of the user with the user-specific perturbation can cause an audio feature detection model to recognize the audio feature in audio samples that include the audio feature and/or to refrain from recognizing the audio feature in audio samples that do not include the audio feature.

Abstract: The current disclosure relates to systems and methods for wakeword or keyword detection in Virtual Personal Assistants (VPAs). In particular, systems and methods are provided for wakeword detection using deep neural networks including a parametric pooling layer, wherein the parametric pooling layer includes trainable parameters, enabling the layer to learn to distinguish between informative feature vectors and non-informative/noisy feature vectors extracted from a variable length acoustic signal. In one example, a parametric pooling layer may aggregate a variable length feature map, comprising a plurality of feature vectors extracted from an acoustic signal, into an embedding vector of pre-determined length, by weighting each of the plurality of feature vectors based on one or more learned parameters in a parametric pooling layer, and aggregating the plurality of weighted feature vectors into the embedding vector.

Abstract: A technique for dynamic sweet spot calibration. The technique includes receiving an image of a listening environment, which may have been captured under poor lighting conditions, and generating a crowd-density map based on the image. The technique further includes setting at least one audio parameter associated with an audio system based on the crowd-density map. At least one audio output signal may be generated based on the at least one audio parameter.

Abstract: A technique for dynamic sweet spot calibration. The technique includes receiving an image of a listening environment, which may have been captured under poor lighting conditions, and generating a crowd-density map based on the image. The technique further includes setting at least one audio parameter associated with an audio system based on the crowd-density map. At least one audio output signal may be generated based on the at least one audio parameter.