Abstract: A method for analyzing environmental sounds to identify repeating noises due to power equipment for purposes of controlling noise pollution is disclosed. Ambient sounds are sampled with microphones and the resulting data analyzed by an edge computing device. The samples are compared to stored data with a convolutional neural network by the edge computing device which also performs pitch shifting and cycle shifting to reduce false negatives. Positive results are reported via LoRa and/or 4G to a cloud platform and/or backend server.

Abstract: A method for analyzing environmental sounds to identify repeating noises due to power equipment for purposes of controlling noise pollution is disclosed. Ambient sounds are sampled with microphones and the resulting data analyzed by an edge computing device. The samples are compared to stored data with a convolutional neural network by the edge computing device which also performs pitch shifting and cycle shifting to reduce false negatives. Positive results are reported via LoRa and/or 4G to a cloud platform and/or backend server.

Abstract: The present disclosure describes methods, devices, and non-transitory computer readable storage medium for recognizing a human action using a graph convolutional network (GCN). The method includes obtaining, by a device, a plurality of joint poses. The device includes a memory storing instructions and a processor in communication with the memory. The method also includes normalizing, by the device, the plurality of joint poses to obtained a plurality of normalized joint poses; extracting, by the device, a plurality of rough features using a modified spatial-temporal GCN (ST-GCN) from the plurality of normalized joint poses; reducing, by the device, a feature dimension of the plurality of rough features to obtain a plurality of dimension-shrunk features; refining, by the device, the plurality of dimension-shrunk features based on a self-attention model to obtain a plurality of refined features; and recognizing, by the device, a human action based on the plurality of refined features.