||Automatic music transcription is one of the most active areas in recent computer music research. Music transcription systems are useful in a wide range of applications including melody extraction, musical database query and interactive music systems. Wave-to-MIDI transcription, being a subset of full automatic music transcription, is a challenging problem to solve by itself. This thesis proposes a Wave-to-MIDI transcription system which is specialized for polyphonic string music, where the input musical signals are converted from acoustic to symbolic representation. The proposed system is divided into two stages. A sinusoidal analysis is performed in the analysis stage. Spectral peaks are extracted with parabolic interpolation and matched across frames to form tracks. In the note detection stage, different smoothing techniques are applied to the detected tracks. Inharmonicity and string spectrum characteristics are incorporated to enhance harmonic detection. Harmonic penalization is also performed to minimize the effect of false octave hits. A MIDI transcription with all detected notes is the result. Experiments show encouraging results for the transcription output using window-based and track-based quantitative analysis. A high percentage of notes are detected by the system for various monophonic and polyphonic inputs. Listening test results also indicate a significant improvement over a previous transcription system.