With the decrease of hardware costs, stationary hydrophones are increasingly deployed in the marine environment to record animal vocalizations amidst ocean noise over an extended period of time. Bioacoustic data collected in this way is an important and practical source to study vocally active marine species and can make an important contribution to ecosystem monitoring. However, a main challenge of this data is the lack of annotation which many supervised neural network models rely on to learn to distinguish between noise and marine animal vocalizations. In this paper, we posit an unsupervised deep embedded clustering based on LSTM autoencoders, that aims to learn the representation of the input audio by minimizing the reconstruction loss and to simultaneously minimize a clustering loss through Kullback–Leibler divergence.