Introduction

Music is ubiquitous ever since humans exist. Prehistoric instruments have been found and thought to be at least 40,000 years old. Music is a pilar of human civilisation; it relates to people’s identities, feelings and thoughts. Hence, means of saving and sharing music are of invaluable importance. The oldest surviving notated music work Hurrian Hymn to Nikkal found on clay tablets dates back to 1400 BC.

Various systems were developped around the globe for visually representing perceived music through the use of written symbols. The modern western notation is the predominent musical notation worldwide for most music genres.

With the rise of technology, audio recordings where introduced as analog signals and eventually as digital signals, providing means for sharing and sauveguarding music aurally.

Music theory and musical notation have been studied for centuries, allowing humans and machines to retrieve music information from common formats. Nevertheless, music processing is a relatively young discipline compared to other subdomains of signal processing such as speech processing; while great results are achieved today in speech recognition, the task of retreiving music information from audio recordings is still far along.

Automatic Music Transcription (AMT) is the task of analyzing musical audio signals and producing the corresponding musical scores. This task has captured researchers interest in the late 20^th century and has become a wide research discipline as many of the problems in this domain remain unsolved. furthermore, strides in the domain of AMT would apply to numerous applications that can facilitate creating, sharing, and learning music.

The scope of this thesis is the domain of Automatic Music Transcription and the underlying tasks. We explore the state of the art and propose an implementation for a subset of the presented methods.