| CPC G06N 3/08 (2013.01) [G10L 25/21 (2013.01); G10L 25/30 (2013.01); G10L 25/51 (2013.01)] | 19 Claims |

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1. A computer-implemented method for training an impulse detection model, the method comprising, at a processor:
receiving audio stream data associated with at least one impulse event;
receiving a label associated with the audio stream data;
detecting, using an onset detector algorithm, at least one peak of the at least one impulse event;
extracting at least one positive sample of the audio stream data associated with the at least one impulse event, wherein the at least one positive sample includes a portion of the audio stream data associated with a first time value of the audio stream data;
labeling the at least one positive sample with the label associated with the audio stream data to yield at least one labeled positive sample;
extracting at least one negative sample of the audio stream data associated with the at least one impulse event, wherein the at least one negative sample does not include the portion of the audio stream data associated with the first time value of the audio stream data;
augmenting machine-learning training data based on the at least one labeled positive sample and the at least one negative sample; and
training, using the processor executing instructions stored on a memory, at least one machine-learning model with the augmented machine-learning training data, wherein the at least one machine-learning model includes an impulsive event detection machine-learning model and is configured to detect, at least, a class of sound associated with the at least one impulse event.
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