Acoustic monitoring of cetaceans is crucial for studying and conserving these animals and their environment. With the rising interest in deciphering dolphin and whale communication, and the promise shown by machine learning solutions in this field, the demand for gathering and processing large vocalization datasets is only increasing. In this paper, we propose an entropy-based spectrogram filtering method that removes noise naturally present in recordings of cetacean vocalizations. This method enhances the visual clarity of cetacean spectrograms, aiding biologists, and improves vocalization detection when using image-based convolutional neural networks. Its implementation focuses on efficiency, processing 256 times faster than similar filters. The resulting spectrograms achieve on average a 98.73% reduction in required storage space and allow for a segmentation technique that reduces labelling and classification times in machine learning solutions.