Dynamic acoustic signature system with sensor fusion for illegal logging in rainforest

A non-transitory computer readable medium, storing instructions for executing a process, the instructions comprising:

• identifying and labeling patterns based on characteristics of sensor data streamed from one or more sensors in a network of sensors, wherein the identifying and labeling the patterns based on the characteristics of the sensor data streamed from the one or more sensors in the network comprises:
• converting acoustic data in the streamed sensor data from time domain to frequency domain;
• deriving frequency domain features from the converted acoustic signals;
• applying dimension reduction to the frequency domain features;
• clustering the dimension reduced frequency domain features;
• and
• applying fingerprint analysis to identify sensor stages;
• processing the identified and labeled patterns to estimate a probability and a lead time for a change to an area associated with the one or more sensors from a current sensor stage to another sensor stage, the processing the identified and labeled patterns comprising a sequential error learning process configured to improve accuracy;
• and
• determining, for neighboring sensors to the one or more sensors in the network of sensors, a probability of a sensor stage change for other areas associated with the neighboring sensors.

An apparatus, comprising:

• a processor, configured to:
• identify and label patterns based on characteristics of sensor data streamed from one or more sensors in a network of sensors, wherein the identifying and labeling the patterns based on the characteristics of the sensor data streamed from the one or more sensors in the network comprises:
• converting acoustic data in the streamed sensor data from time domain to frequency domain;
• deriving frequency domain features from the converted acoustic signals;
• applying dimension reduction to the frequency domain features;
• clustering the dimension reduced frequency domain features;
• and
• applying fingerprint analysis to identify sensor stages;
• process the identified and labeled patterns to estimate a probability and a lead time for a change to an area associated with the one or more sensors from a current sensor stage to another sensor stage, the processing the identified and labeled patterns comprising a sequential error learning process configured to improve accuracy;
• and
• determine, for neighboring sensors to the one or more sensors in the network of sensors, a probability of a sensor stage change for other areas associated with the neighboring sensors.