Guitar tablature is a musical notation that uses six lines to represent the strings, while numbers on those lines show which frets to press. Digital tablature interfaces enhance the playing and learning experience through synchronized tablature playback, drum tracks, and more. Tablature interfaces are generally operated by touch or conventional mouse and keyboard input. Controlling playback, navigating through songs, and configuring the interface can result in inconvenient context switches since guitarists regularly switch between playing the guitar and interacting with the interface via mouse and keyboard. In this thesis, we use the audio input of an unmodified electric guitar to investigate possibilities for navigating tablature interfaces while avoiding such context switches. Our goal is to facilitate the use of tablature interfaces while maintaining a consistent interaction mode. To achieve this goal, we explored different algorithms to determine which approach works best for our use case of audio-matching tasks. These algorithms compare the guitarist's audio input with synthetically generated song audio and produce a compliance score, which aims to enable navigation for any opened song in the tablature interface. Furthermore, we utilize audio matching to achieve accessible song selection without using a mouse or keyboard. The implementation of bar-specific navigation within a song posed several challenges. First, the recorded audio input differs too much from the synthetically generated sound to enable reliable matching. Second, modern songs have too many bars in which the guitar notes differ little or not at all. These factors and others made it impossible to use a robust audio-based navigation system for jumping to specific bars. However, a bookmark system enables song selection without letting go of the guitar, and predefined commands allow control of many functions. That way, the user can interact with the system without using a mouse or keyboard, relying exclusively on the guitar as the input device. This thesis highlights the opportunities and challenges of integrating audio-based controls into tablature interfaces. It might inspire future work to explore more intuitive ways for musicians to interact with digital systems.