Systems, methods, and non-transitory computer readable media are provided for monitoring the state of a periodic system. A computer implemented method for modeling a state of a periodic system includes inputting a spectrogram sequence to a machine-learning model trained to generate a latent representation from the spectrogram sequence. The spectrogram sequence includes a plurality of audio spectrograms representing sound generated by a periodic system. The method includes outputting the latent representation from the machine learning model. The method includes concatenating the latent representation with environmental data describing an environment of the periodic system, together defining an input sequence. The method includes inputting the input sequence to a predictor model trained to predict a state of the periodic system from the input sequence. The method also includes predicting the state of the periodic system with the predictor model.
SEMI-SUPERVISED AUDIO REPRESENTATION LEARNING FOR MODELING BEEHIVE STRENGTHS