Score-Informed Audio Parameterization of Music Signals

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This project deals with the development of robust and practical methods for the automated parameterization of music signals. In particular, the goal is to identify and reconstruct signal components that correspond to individual notes events or entire melodic and instrumental voices - a task being closely related to what is commonly known as source separation in general audio signal processing. In the case of music, a key challenge is to allow complex superpositions of closely related musical sources including singing/instrumental voices and accompaniments in music such as piano songs or operas. Without additional knowledge, a decomposition of a one- or two-channel audio recording into such voices is hardly possible. Therefore, in this project, we want to follow an informed approach, where additional score information is exploited in the audio parameterization process. On the basis of automatically computed score-audio alignments, the score information is used to specify and guide the parameterization process as well as to support the signal analysis and reconstruction steps. In addition to fundamental research on signal modeling and parameter optimization, this project is also concerned with the development of novel applications demonstrating the practical relevance and ensuring the sustainability of the project.