term "automatic music transcription" was first used by audio researchers James A. Moorer, Martin Piszczalski, and Bernard Galler in 1977. With their knowledge of digital audio engineering, these researchers believed that a computer could be programmed to analyze a digital recording of music such that the pitches of melody lines and chord patterns could be detected, along with the rhythmic accents of percussion instruments. The task of automatic music transcription concerns two separate activities: making an analysis of a musical piece, and printing out a score from that analysis. This was not a simple goal, but one that would encourage academic research for at least another three decades. Because of the close scientific relationship of speech to music, much academic and commercial research that was directed toward the more financially resourced speech recognition technology would be recycled into research about music recognition technology. While many musicians and educators insist that manually doing transcriptions is a valuable exercise for developing musicians, the motivation for automatic music transcription remains the same as the motivation for sheet music: musicians who do not have intuitive transcription skills will search for sheet music or a chord chart, so that they may quickly learn how to play a song. A collection of tools created by this ongoing research could be of great aid to musicians. Since much recorded music does not have available sheet music, an automatic transcription device could also offer transcriptions that are otherwise unavailable in sheet music. To date, no software application can yet completely fulfill James Moorer’s definition of automatic music transcription. However, the pursuit of automatic music transcription has spawned the creation of many software applications that can aid in manual transcription. Some can slow down music while maintaining original pitch and octave, some can track the pitch of melodies, some can track the chord changes, and others can track the beat of music. Automatic transcription most fundamentally involves identifying the pitch and duration of the performed notes. This entails tracking pitch and identifying note onsets. After capturing those physical measurements, this information is mapped into traditional music notation, i.e., the sheet music. Digital Signal Processing is the branch of engineering that provides software engineers with the tools and algorithms needed to analyze a digital recording in terms of pitch (note detection of melodic instruments), and the energy content of un-pitched sounds (detection of percussion instruments). Musical recordings are sampled at