Deep clustering was applied to unlabeled, automatically detected signals in a coral reef soundscape to distinguish fish pulse calls from segments of whale song. Deep embedded clustering (DEC) learned latent features of the signals and formed clusters using fixed-length power spectrograms of the signals. Handpicked spectral and temporal features were also extracted and clustered with Gaussian mixture models (GMM) and conventional clustering. DEC, GMM, and conventional clustering were tested on simulated datasets of fish pulse calls and whale song units with randomized bandwidth, duration, and SNR. Both GMM and DEC achieved high accuracy and identified clusters with fish calls, whale song units, and simultaneous fish calls and whale song units. Conventional clustering methods K-means and hierarchical agglomerative clustering had low accuracy in scenarios with unequally sized clusters or multiple signals. Experimentally detected fish pulse calls and whale song units recorded near Hawaii in February–March 2020 were clustered with DEC, GMM, and conventional clustering. DEC demonstrated the highest accuracy on a small, manually labeled dataset and successfully separated clusters of fish calls and whale song units. GMM achieved high recall for detecting whale song units but accuracy. Both GMM and DEC overpredicted the number of whale song unit signals.