\pagestyle{fancy}
\cleardoublepage\chapter{Conclusion and Future Work}\label{final}
%\Quote{Motivation is what gets you started. Habit is what keeps you going.}{Jim Rynn}
\Quote{If you're not prepared to be wrong, you'll never come up with anything original.}{Sir Ken Robinson}

\section{Conclusion}\label{final.conclusion}
The goal of this thesis was to show whether methods from image processing can help to enhance spectrograms that are further used for feature extraction. We investigated two tasks, chord recognition and onset detection. The image processing methods considered in the scope of this thesis included diffusion processes (edge-enhancing diffusion and coherence-enhancing diffusion), mathematical morphology (dilation, erosion, closing, opening and skeletonization) as well as NL-means in various forms. Furthermore, we used the algorithms in their basic form which means that no knowledge of audio processing was included in the algorithmic design. In the case of NL-means, several approaches already available in the literature were implemented where their main purpose is to speed up the runtime of the NL-means algorithm that is computationally expensive. In addition to that, we proposed a new approach to skeletonization: parabolic skeletonization.

Modified versions that use a directional preference when processing a spectrogram were also implemented for all algorithms. This modification is important since chord recognition relies on horizontal structures and onset detection on vertical structures. For NL-means, this directional preference can be realized by changing the similarity window from a quadratic shape to a rectangular one. For diffusion processes, we introduced a modified diffusion tensor to make sure that a certain direction is preferred.

For chord recognition, image processing algorithms could significantly improve the spectrograms in most of the cases according to the quality measure under consideration. The task of onset detection is more difficult. As a consequence, only marginal improvements were achieved. For some of the testfiles, even none of the algorithms investigated here could improve the result. In fact, image processing methods usually try to repair noise that appears locally. Since onset detection seems to be insensitive to this kind of deterioration, image processing methods cannot fruitfully serve as a preprocessing step and the focus should lie in improving the algorithms used for onset detection.

Usually no ground truth is available in case of chord recognition as well as for onset detection. Thus, we proposed for each image processing algorithm a corresponding parameter set that can be used as a guideline for the application on real-world data.

%In our experiments, we have seen that mainly diffusion algorithms with preference for horizontal or vertical direction (depending on the problem) can enhance the spectrograms. Algorithms without directional preference did in general not succeed to improve the quality of the spectrograms.
%
In our experiments, we have seen that mainly diffusion algorithms with preference for horizontal or vertical direction (depending on the problem) can enhance the spectrograms where algorithms without directional preference did in general not succeed to do so.

\section{Future Work}\label{final.futurework}
The image processing algorithms investigated in this thesis were used in their basic form. Besides slight modifications to meet certain requirements in the experiments, no further knowledge is involved. Incorporating knowledge about music and audio processing into the algorithms might enhance the overall result and may lead to a smaller runtime due to assumptions or simplifications potentially introduced by this knowledge. But also knowledge from music theory could be helpful to adapt the behavior of algorithms, e.g.\ usual chord progression patterns for chord recognition to reduce the impact of progressions that are unlikely.

It is also interesting to investigate how various image processing algorithms perform when applied consecutively on the chord recognition or onset detection problem. Such processing chains were shortly touched in Chapters \ref{chord_experiments} and \ref{onset_experiments}. However, a deep analysis on the impact of such chains would have been beyond the scope of this thesis. At least for the task of chord recognition, improvements can be expected by the consecutive application of two or more algorithms.

NL-means produces state-of-the-art results for image denoising tasks and many improvements have been contributed. To provide a wide variety of different image processing methods in the context of this thesis, only some of the approaches to improve NL-means were investigated \cite{zimmer08, mahmoudi05} and others were left out of consideration, e.g.\ \cite{orchard08}. While the algorithm proposed by Mahmoudi et al.\ \cite{mahmoudi05} is one of the best NL-means speed-up strategies, the approach is somewhat heuristic. Orchard et al.\ \cite{orchard08} suggest to use the singular value decomposition (SVD) and to make use of its statistical properties. Their experiments showed that this method significantly improves the denoising effect due to a refined discrimination between similar and dissimilar pixel neighborhoods. It would be interesting to explore how this approach performs on spectrograms. At least for chord recognition, a changed similarity measure might give better inpainting results.
%It would be interesting how this NL-means variant performs on the spectrograms.
%Furthermore, Zimmer et al.\ proposed in \cite{zimmer08} another function for the locally adaptive weighting which is an interesting point to explore.

The algorithms presented in this thesis rely on several parameters. While we have fixed most of them, it might be possible to achieve better results by tweaking some parameters or by choosing them adaptively in a similar fashion like the locally adaptive weighting for NL-means. This task may also incorporate knowledge from audio processing to find better parameter settings.

Beyond image processing algorithms, the usage of PLP curves as introduced in \cite{grosche09-2} instead of novelty curves as onset detector might give advantages since a PLP curve is more robust to outliers and reveals musically meaningful information.% This even holds if we start with poor onset information.