try to fix references etc.

paper/paper.md

@@ -29,7 +29,7 @@ Music Information Retrieval (MIR) stands as a dedicated research field focused o
 organizing, analyzing, retrieving, and generating data related to music. Key tasks within MIR include beat tracking, 
 structural analysis, chord recognition, melody extraction, and source separation, just to name a few. These tasks
 involve extracting musically relevant information from audio recordings, typically accomplished by transforming music 
-signals into feature representations such as spectrograms, chromagrams, or tempograms~\citep{Mueller15_FMP_SPRINGER}.
+signals into feature representations such as spectrograms, chromagrams, or tempograms [@Mueller15_FMP_SPRINGER].
 Furthermore, musically relevant annotations such as beats, chords, keys, or structure boundaries become
 indispensable for training and evaluating MIR approaches.
 
@@ -39,20 +39,46 @@ alongside conventional data visualization techniques, data sonification techniqu
 for providing auditory feedback on extracted features or annotated information. This is particularly advantageous given 
 the finely tuned human perception to subtle variations in frequency and timing within the musical domain.
 
-This paper introduces \emph{libsoni}, an open-source Python toolbox tailored for the sonification of music annotations 
+This paper introduces *libsoni*, an open-source Python toolbox tailored for the sonification of music annotations 
 and feature representations. By employing explicit and easy-to-understand sound synthesis techniques, libsoni offers 
 functionalities for generating and triggering sound events, enabling the sonification of spectral, harmonic, tonal, 
 melodic, and rhythmic aspects. Unlike existing software libraries focused on  creative applications of sound generation,
 libsoni is designed to meet the specific needs of MIR researchers and educators. It aims to simplify the process of 
 music exploration, promoting a more intuitive and efficient approach to data analysis by enabling users to interact with 
 their data in acoustically meaningful ways. As a result, libsoni not only improves the analytical capabilities of music
 scientists but also opens up new avenues for innovative music analysis and discovery.
-%
+
 Furthermore, libsoni provides well-documented and stand-alone functions covering all essential building blocks crucial 
 for both sound generation and sonification, enabling users to efficiently apply and easily extend the methods. 
 Additionally, the toolbox includes educational Jupyter notebooks with illustrative code examples demonstrating the 
 application of sonification and visualization methods to deepen understanding within specific MIR scenarios.
 
+# Design Choices
+When designing the Python toolbox libsoni, we had several objectives in mind. Firstly, we aimed to maintain close 
+connections with existing sonification methods provided in in librosa[@McFeeRLEMBN15_librosa_Python] and 
+libfmp[@MuellerZ21_libfmp_JOSS]. Secondly, we re-implemented and included all necessary components 
+(e.g., sound generators based on sinusoidal models and click sounds), even though similar basic functionality is 
+available in other Python packages such as librosa and libfmp. By doing so, libsoni offers a coherent API along with 
+convenient but easily modifiable parameter presets. Additionally, the implementations are more efficient than previous 
+software. Thirdly, we adopted many design principles suggested by librosa[@McFeeRLEMBN15_librosa_Python]  
+and detailed in [@McFeeKCSBB19_OpenSourcePractices_IEEE-SPM] to lower the entry barrier for students and 
+researchers who may not be coding experts. This includes maintaining an explicit and straightforward programming 
+style with a flat, functional hierarchy to facilitate ease of use and comprehension. The source code for
+libsoni, along with comprehensive API documentation, is publicly accessible through a dedicated GitHub
+repository [^3]. We showcase all components, including introductions to MIR scenarios, illustrations, and sound examples
+via Jupyter notebooks.  Finally, we have included the toolbox in the Python Package Index (PyPI), enabling
+installation with the standard Python package manager, pip [^4].
+
+[^3]: <https://github.com/groupmm/libsoni>
+[^4]: <https://groupmm.github.io/libsoni>
+
 # Acknowledgements
+The libsoni package originated from collaboration with various individuals over the past years. We extend our gratitude 
+to former and current students, collaborators, and colleagues, including Jonathan Driedger, Angel Villar-Corrales, and 
+Tim Zunner, for their support and influence in creating this Python package. This work was funded by the Deutsche 
+Forschungsgemeinschaft (DFG, German Research Foundation) under Grant No. 500643750 (DFG-MU 2686/15-1) and Grant No. 
+328416299 (MU 2686/10-2). The International Audio Laboratories Erlangen are a joint institution of the 
+Friedrich-Alexander-Universit\"at Erlangen-N\"urnberg (FAU) and Fraunhofer Institute for Integrated Circuits IIS.
+
 
 # References

paper/references.bib

@@ -0,0 +1,44 @@
+@article{MuellerZ21_libfmp_JOSS,
+  author    = {Meinard M{\"u}ller and Frank Zalkow},
+  title     = {{libfmp}: {A} {P}ython Package for Fundamentals of Music Processing},
+  journal   = {Journal of Open Source Software ({JOSS})},
+  volume    = {6},
+  number    = {63},
+  year      = {2021},
+  pages     = {3326:1--5},
+  doi 		= {10.21105/joss.03326},
+  url-pdf   = {2021_MuellerZalkow_libfmp_JOSS.pdf},
+  url-demo = {https://github.com/meinardmueller/libfmp}
+}
+
+@article{McFeeKCSBB19_OpenSourcePractices_IEEE-SPM,
+  author    = {Brian McFee and Jong Wook Kim and Mark Cartwright and Justin Salamon and Rachel M. Bittner and Juan Pablo Bello},
+  title     = {Open-Source Practices for Music Signal Processing Research: Recommendations for Transparent, Sustainable, and Reproducible Audio Research},
+  journal   = {{IEEE} Signal Processing Magazine},
+  volume    = {36},
+  number    = {1},
+  pages     = {128--137},
+  year      = {2019},
+  doi       = {10.1109/MSP.2018.2875349}
+}
+
+@book{Mueller15_FMP_SPRINGER,
+  author    = {Meinard M\"{u}ller},
+  title     = {Fundamentals of Music Processing -- Audio, Analysis, Algorithms, Applications},
+  type      = {Monograph},
+  year      = {2015},
+  isbn      = {978-3-319-21944-8},
+  publisher = {Springer Verlag},
+  pages     = {1--480},
+  doi       = {10.1007/978-3-319-21945-5}
+}
+
+@inproceedings{McFeeRLEMBN15_librosa_Python,
+  author    = {Brian McFee and Colin Raffel and Dawen Liang and Daniel P.W. Ellis and Matt McVicar and Eric Battenberg and Oriol Nieto},
+  title     = {Librosa: {A}udio and Music Signal Analysis in {P}ython},
+  booktitle = {Proceedings the Python Science Conference},
+  pages     = {18--25},
+  address   = {Austin, Texas, USA},
+  year      = {2015},
+  doi       = {10.25080/Majora-7b98e3ed-003}
+}