House mice (Mus musculus), like other rodents, communicate using sonic and ultrasonic vocalizations (USVs), but their functions are still poorly understood. One of the main challenges for studying any acoustic communication is processing and analyzing audio files. Our aims here are to provide a critical and comprehensive review of the new bioacoustic tools available for processing and analyzing recordings of mouse vocalizations. We consider each method as used in a serial data processing pipeline and how to minimize errors at each step to prevent error propagation (or error cascades). First, we review methods for processing audio files of recordings of mice. We compare conventional approaches for visualizing vocalizations (time-frequency representations) with an alternative method adapted to the mouse auditory system. We compare machine learning (ML) and signal processing methods for automating USV detection and emphasize the need for better methods for denoising audio files and reliable frequency contour (ridge) tracking and feature extraction. Second, we review methods for analyzing detected USVs, focusing on classification and sequencing approaches. Classifying USVs is a challenging task because, while some calls are discrete, others show graded variation within and between call classes. We compare supervised classifications and unsupervised labeling, and we emphasize the importance of reliable manual (researcher-based) methods as a gold standard for automated ML approaches. We review classifications of mouse vocalizations in the literature, and we propose a new hierarchical framework for the classification of USVs. We examine methods for sequencing USVs and consider their relative advantages. Finally, we address the unresolved technological challenges for these methods to study rodent vocalizations and propose potential solutions for the future.