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Saarland University\\
Faculty of Natural Sciences and Technology I\\
Department of Computer Science\\
Master's Program in Visual Computing\\
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\sout{\hspace*{\fill}}\textbf{ Master's Thesis }\sout{\hspace*{\fill}}\\
\Huge
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%\textbf{ Enhancing Spectrograms \\ for Feature Extraction \\ in Music Information Retrieval \\ using Methods from Image Processing }\\
\textbf{ Can Image Processing Help \\ Music Information Retrieval? \\ {\Large Enhancing Spectrograms for Feature Extraction} }\\
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\LARGE
\textbf{ Jan Hendrik Dithmar }\\
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\number\year-\number\month-\number\day\\
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     Supervisor             & PD Dr.\ Martin Welk\\
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     Advisor                & Peter Grosche, M.Sc. \\
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     Reviewers              & PD Dr.\ Martin Welk\\
                            & PD Dr.\ Meinard M{\"u}ller
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\section*{Affidavit}
I hereby declare that this master's thesis has been written only by the undersigned and without any assistance from third parties.\\
Furthermore I confirm that no sources have been used in the preparation of this thesis other than those indicated in the thesis itself.
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Saarbr\"ucken, \number\year-\number\month-\number\day

\section*{Declaration of Consent}
Herewith I agree that this thesis will be made available through the library of the Computer Science Department. This consent explicitly includes both the printed, as well as the electronic form attached to this thesis.\\
I confirm that the electronic and the printed version are of identical content.\\
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Saarbr\"ucken, \number\year-\number\month-\number\day

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	{\Large To Nellie and Noah}
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\section*{Abstract}
In today's world, a lot of information is accessible in a digital form. This is especially true for images or music. To make use of this variety of data, it has to be processed meaningfully which leads to the task of information retrieval. The field of music information retrieval (MIR) allows the user to access and explore music. For example, the user could get additional information about melodic and harmonic structures or rhythm and tempo. This is fruitful information for music analysis. Another application is to find a convenient way to navigate inside a piece of music. This could be realized by searching for structures like similar rhythmic patterns or similar chord progressions and giving the user the opportunity to jump directly to certain similar parts. However, such purely content-based tasks are very tough.% This is due to the complexity of music data.

For the context of this thesis, we focus on chord recognition and onset detection and try to enhance spectrograms that are computed from the music data. For chord recognition, we have to enhance frequency contributions that belong to the notes in a chord at a certain time. In contrast, in the context of onset detection it is important when an onset occurs, not which frequencies contribute to it. In other words, vertical structures have to be enhanced for onset detection. % Onset detection relies on vertical structures because here it is important when an onset occurs and not which frequencies contribute to it.

We are looking at the different problems mainly from an image processing viewpoint. We apply several image processing methods to spectrograms where we want to investigate whether we can improve the quality of the spectrogram for further processing by feature extraction algorithms.

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\section*{Acknowledgements}
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I would like to express my sincere thanks to PD Dr.\ Martin Welk for the excellent mentoring and support during the development of this thesis. His encouragement and our vivid discussions as well as the friendly atmosphere in the Mathematical Image Analysis Group greatly assisted me in my work. Furthermore, I gratefully acknowledge that Prof.\ Dr.\ Joachim Weickert provided me with a topic that fits both my interests in image processing and in music. I also like to thank PD Dr.\ Meinard M{\"u}ller for the cooperation with the Mathematical Image Analysis Group (which makes it possible for me to work on this topic) and for consenting to review my thesis. Many thanks are also devoted to Peter Grosche for giving me very good insights into music processing and for the great support concerning MATLAB.
\\\\
Special thanks go to my family who enabled me to perform my studies and to pursue my musical career as I did and who therefore also significantly contributed to the success I have enjoyed so far.

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