added complete reference list with script to generate it

aspcol/distance.py

@@ -5,7 +5,7 @@
 References
 ----------
 [herdinCorrelation2005] M. Herdin, N. Czink, H. Ozcelik, and E. Bonek, 'Correlation matrix distance, a meaningful measure for evaluation of non-stationary MIMO channels,' in 2005 IEEE 61st Vehicular Technology Conference, May 2005, pp. 136-140 Vol. 1. doi: 10.1109/VETECS.2005.1543265. `[link] <https://doi.org/10.1109/VETECS.2005.1543265>`_ \n
-[forstnermetric2003] W. Förstner and B. Moonen, 'A metric for covariance matrices,' in Geodesy-The Challenge of the 3rd Millennium, E. W. Grafarend, F. W. Krumm, and V. S. Schwarze, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 299–309. doi: 10.1007/978-3-662-05296-9_31. `[link] <(doi.org/10.1007/978-3-662-05296-9_31>`_ \n
+[forstnermetric2003] W. Förstner and B. Moonen, 'A metric for covariance matrices,' in Geodesy-The Challenge of the 3rd Millennium, E. W. Grafarend, F. W. Krumm, and V. S. Schwarze, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 299–309. doi: 10.1007/978-3-662-05296-9_31. `[link] <https://doi.org/10.1007/978-3-662-05296-9_31>`_ \n
 [duchiDerivations2016] J. Duchi, 'Derivations for Linear Algebra and Optimization, 2016. `[link] <https://web.stanford.edu/~jduchi/projects/general_notes.pdf>`_ \n
 [absilOptimization2008] P.-A. Absil, R. Mahony, and R. Sepulchre, Optimization algorithms on matrix manifolds. Princeton, N.J. ; Woodstock: Princeton University Press, 2008. \n
 """

aspcol/soundfieldestimation.py

@@ -7,7 +7,7 @@
 References
 ----------
 [uenoKernel2018] N. Ueno, S. Koyama, and H. Saruwatari, “Kernel ridge regression with constraint of Helmholtz equation for sound field interpolation,” in 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC), Tokyo, Japan: IEEE, Sep. 2018, pp. 436–440. doi: 10.1109/IWAENC.2018.8521334. `[link] <https://doi.org/10.1109/IWAENC.2018.8521334>`_ \n
-[brunnstromBayesianSubmitted] J. Brunnström, M. B. Mo/ller, and M. Moonen, “Bayesian sound field estimation using moving microphones,” IEEE Open Journal of Signal Processing, submitted. \n
+[brunnstromBayesianSubmitted] J. Brunnström, M. B. Møller, and M. Moonen, “Bayesian sound field estimation using moving microphones,” IEEE Open Journal of Signal Processing, submitted. \n
 [katzbergSpherical2021] F. Katzberg, M. Maass, and A. Mertins, “Spherical harmonic representation for dynamic sound-field measurements,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun. 2021, pp. 426–430. doi: 10.1109/ICASSP39728.2021.9413708. `[link] <https://doi.org/10.1109/ICASSP39728.2021.9413708>`_ \n
 """
 import numpy as np

docs/source/collect_references.py

@@ -0,0 +1,114 @@
+"""
+Run this script to collect all references from the docs and write them out at the end of the front page of the documentation. 
+"""
+
+import pathlib
+import os
+import sys
+import importlib
+import numpy as np
+
+def main():
+    current_dir = pathlib.Path(__file__).parent
+    package_dir = current_dir.parent.parent / "aspcol"
+    output_file_path = current_dir / "references.rst"
+
+    sys.path.append(str(package_dir))
+
+    module_names = []
+    for f in package_dir.iterdir():
+        if f.suffix == ".py": #only search top level
+            if f.stem != "__init__":
+                module_names.append(f.stem)
+
+    modules = [importlib.import_module(f"aspcol.{m}") for m in module_names]
+    docs = [m.__doc__ for m in modules]
+
+    all_refs = get_all_refs(docs)
+    ids = get_ref_identifiers(all_refs)
+
+    ref_list = extract_list_according_to_ids(all_refs, ids)
+    ref_list = "".join(ref_list)
+
+    with open(output_file_path, "w", encoding="utf-8") as f:
+        f.write(f"References\n----------\n{ref_list}")
+
+
+def find_start_idxs(all_refs, ids):
+    # Connect start idxs to IDs and put in dictionary
+    # Put all start idxs (including duplicates) in a list
+    id_start_idxs = {}
+    all_start_idxs = []
+    for ref_id in ids:
+        search_start = 0
+        while True:
+            idx = all_refs.find(f"[{ref_id}]", search_start)
+            if idx == -1:
+                break
+            search_start = idx + len(ref_id) + 2
+            #str_to_check = str_to_check[idx + len(ref_id) + 2:]
+            all_start_idxs.append(idx)
+            if ref_id not in id_start_idxs:
+                id_start_idxs[ref_id] = idx
+
+    # Get end idxs for all start idxs
+    # Manually correct the end idx of the last reference
+    all_idxs = {}
+    all_start_idxs = np.sort(all_start_idxs)
+    for ref_id, start_idx in id_start_idxs.items():
+        idx_diff = all_start_idxs - start_idx
+        idx_diff[idx_diff <= 0] = int(1e8)
+        end_idx = all_start_idxs[np.argmin(idx_diff)]
+
+        if end_idx == 0:
+            end_idx = len(all_refs)
+        all_idxs[ref_id] = (start_idx, end_idx)
+
+    return all_idxs
+
+
+def extract_list_according_to_ids(all_refs, ids):
+    all_idxs = find_start_idxs(all_refs, ids)
+    all_idxs = dict(sorted(all_idxs.items()))
+    #num_refs = len(ids)
+
+    # ref_pos = [all_refs.find(ref_id) for ref_id in ids]
+    # ref_pos.append(len(all_refs)+1) #to include the last reference
+    # ref_pos = np.sort(ref_pos)
+    # ref_pos -= 1 #to include the "[" in the reference
+    ref_list = [all_refs[start_idx:end_idx] for ref_id, (start_idx, end_idx) in all_idxs.items()]
+
+    #ref_list = [all_refs[ref_pos[i]:ref_pos[i+1]] for i in range(num_refs)]
+    ref_list = [ref.replace("\n", "") for ref in ref_list]
+    ref_list = [ref.strip() for ref in ref_list]
+    ref_list = [f"{ref}\n\n" for ref in ref_list]
+    return ref_list
+
+def get_all_refs(docs):
+    all_refs = []
+    for txt in docs:
+        split_txt = txt.split("References\n----------\n")
+        if len(split_txt) > 1:
+            references = split_txt[1]
+            all_refs.append(references)
+    full_ref_list = "\n".join(all_refs)
+    return full_ref_list
+
+
+def get_ref_identifiers(references):
+    ids = []
+    #references.split("[]")
+    for str_part in references.split("["):
+        if len(str_part) > 1:
+            part_list = str_part.split("]")
+            if len(part_list) > 1:
+                id = part_list[0]
+                if id != "link":
+                    if id not in ids:
+                        ids.append(id)
+    return ids
+
+
+
+if __name__ == "__main__":
+    main()

docs/source/index.rst

@@ -27,6 +27,13 @@ API Reference
    aspcol.soundfieldestimation
    aspcol.utilities
 
+Reference list
+==============
+A full list of the papers relevant to the implemented algorithms in this package can be found at the following link. 
+
+.. toctree::
+
+   references
 
 License
 =======

docs/source/references.rst

@@ -0,0 +1,56 @@
+References
+----------
+[absilOptimization2008] P.-A. Absil, R. Mahony, and R. Sepulchre, Optimization algorithms on matrix manifolds. Princeton, N.J. ; Woodstock: Princeton University Press, 2008.
+
+[antweilerNLMStype2008] C. Antweiler, A. Telle, and P. Vary, “NLMS-type system identification of MISO systems with shifted perfect sequences,” Proceedings of the International Workshop on Acoustic Echo and Noise Control (IWAENC), Seattle, WA, Sep. 2008.
+
+[antweilerSystem2014] C. Antweiler, S. Kuehl, B. Sauert, and P. Vary, “System identification with perfect sequence excitation - efficient NLMS vs. inverse cyclic convolution,” in Speech Communication; 11. ITG Symposium, Sep. 2014, pp. 1–4.
+
+[atkinsApproximate2013] J. Atkins, A. Strauss, and C. Zhang, “Approximate convolution using partitioned truncated singular value decomposition filtering,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, pp. 176–180. doi: 10.1109/ICASSP.2013.6637632. `[link] <https://doi.org/10.1109/ICASSP.2013.6637632>`_
+
+[brunnstromBayesianSubmitted] J. Brunnström, M. B. Møller, and M. Moonen, “Bayesian sound field estimation using moving microphones,” IEEE Open Journal of Signal Processing, submitted.
+
+[brunnstromSignaltointerferenceplusnoise2023] J. Brunnström, T. van Waterschoot, and M. Moonen, “Signal-to-interference-plus-noise ratio based optimization for sound zone control,” IEEE Open Journal of Signal Processing, vol. 4, pp. 257–266, 2023, doi: 10.1109/OJSP.2023.3246398. `[link] <https://doi.org/10.1109/OJSP.2023.3246398>`_
+
+[brunnstromSound2023] J. Brunnström, T. van Waterschoot, and M. Moonen, “Sound zone control for arbitrary sound field reproduction methods,” in European Signal Processing Conference (EUSIPCO), Helsinki, Finland, Sep. 2023. `[link] <https://doi.org/10.23919/EUSIPCO58844.2023.10289995>`_
+
+[brunnstromVariable2022] J. Brunnström, S. Koyama, and M. Moonen, “Variable span trade-off filter for sound zone control with kernel interpolation weighting,” in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2022, pp. 1071–1075. doi: 10.1109/ICASSP43922.2022.9746550. `[link] <https://doi.org/10.1109/ICASSP43922.2022.9746550>`_
+
+[chenShrinkage2010] Y. Chen, A. Wiesel, Y. C. Eldar, and A. O. Hero, “Shrinkage Algorithms for MMSE Covariance Estimation,” IEEE Trans. Signal Process., vol. 58, no. 10, pp. 5016–5029, Oct. 2010, doi: 10.1109/TSP.2010.2053029. `[link] <https://doi.org/10.1109/TSP.2010.2053029>`_
+
+[crochiereWeighted1980] R. Crochiere, “A weighted overlap-add method of short-time Fourier analysis/synthesis,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 28, no. 1, pp. 99–102, Feb. 1980, doi: 10.1109/TASSP.1980.1163353. `[link] <https://doi.org/10.1109/TASSP.1980.1163353>`_
+
+[dinizAdaptive2020] P. S. R. Diniz, Adaptive filtering: algorithms and practical implementation. Cham: Springer International Publishing, 2020. doi: 10.1007/978-3-030-29057-3. `[link] <https://link.springer.com/book/10.1007/978-3-030-29057-3>`_
+
+[duchiDerivations2016] J. Duchi, 'Derivations for Linear Algebra and Optimization, 2016. `[link] <https://web.stanford.edu/~jduchi/projects/general_notes.pdf>`_
+
+[forstnermetric2003] W. Förstner and B. Moonen, 'A metric for covariance matrices,' in Geodesy-The Challenge of the 3rd Millennium, E. W. Grafarend, F. W. Krumm, and V. S. Schwarze, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 299–309. doi: 10.1007/978-3-662-05296-9_31. `[link] <https://doi.org/10.1007/978-3-662-05296-9_31>`_
+
+[hahnSimultaneous2018] N. Hahn and S. Spors, “Simultaneous measurement of spatial room impulse responses from multiple sound sources using a continuously moving microphone,” in 2018 26th European Signal Processing Conference (EUSIPCO), Sep. 2018, pp. 2180–2184. doi: 10.23919/EUSIPCO.2018.8553532. `[link] <https://doi.org/10.23919/EUSIPCO.2018.8553532>`_
+
+[herdinCorrelation2005] M. Herdin, N. Czink, H. Ozcelik, and E. Bonek, 'Correlation matrix distance, a meaningful measure for evaluation of non-stationary MIMO channels,' in 2005 IEEE 61st Vehicular Technology Conference, May 2005, pp. 136-140 Vol. 1. doi: 10.1109/VETECS.2005.1543265. `[link] <https://doi.org/10.1109/VETECS.2005.1543265>`_
+
+[itoFeedforward2019] H. Ito, S. Koyama, N. Ueno, and H. Saruwatari, “Feedforward spatial active noise control based on kernel interpolation of sound field,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom: IEEE, May 2019, pp. 511–515. doi: 10.1109/ICASSP.2019.8683067. `[link] <https://doi.org/10.1109/ICASSP.2019.8683067>`_
+
+[jalmbyFast2023] M. Jälmby, F. Elvander, and T. van Waterschoot, “Fast low-latency convolution by low-rank tensor approximation,” in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes, Greece, Jun. 2023. `[link] <https://doi.org/10.1109/ICASSP49357.2023.10095908>`_
+
+[jalmbyLowrank2021] M. Jälmby, F. Elvander, and T. van Waterschoot, “Low-rank tensor modeling of room impulse responses,” in 2021 29th European Signal Processing Conference (EUSIPCO), Aug. 2021, pp. 111–115. doi: 10.23919/EUSIPCO54536.2021.9616075. `[link] <https://doi.org/10.23919/EUSIPCO54536.2021.9616075>`_
+
+[katzbergSpherical2021] F. Katzberg, M. Maass, and A. Mertins, “Spherical harmonic representation for dynamic sound-field measurements,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun. 2021, pp. 426–430. doi: 10.1109/ICASSP39728.2021.9413708. `[link] <https://doi.org/10.1109/ICASSP39728.2021.9413708>`_
+
+[koyamaSpatial2021] S. Koyama, J. Brunnström, H. Ito, N. Ueno, and H. Saruwatari, “Spatial active noise control based on kernel interpolation of sound field,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 3052–3063, Aug. 2021, doi: 10.1109/TASLP.2021.3107983. `[link] <https://doi.org/10.1109/TASLP.2021.3107983>`_
+
+[ledoitQuadratic2020] O. Ledoit and M. Wolf, “Quadratic shrinkage for large covariance matrices,” Dec. 2020, doi: 10.5167/UZH-176887. `[link] <https://doi.org/10.5167/UZH-176887>`_
+
+[leeFast2020] T. Lee, L. Shi, J. K. Nielsen, and M. G. Christensen, “Fast generation of sound zones using variable span trade-off filters in the DFT-domain,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 363–378, Dec. 2020, doi: 10.1109/TASLP.2020.3042701. `[link] <https://doi.org/10.1109/TASLP.2020.3042701>`_
+
+[paleologuLinear2018] C. Paleologu, J. Benesty, and S. Ciochină, “Linear system identification based on a Kronecker product decomposition,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 10, pp. 1793–1808, Oct. 2018, doi: 10.1109/TASLP.2018.2842146. `[link] <https://doi.org/10.1109/TASLP.2018.2842146>`_
+
+[ribeiroKernel2020] J. G. C. Ribeiro, N. Ueno, S. Koyama, and H. Saruwatari, “Kernel interpolation of acoustic transfer function between regions considering reciprocity,” in 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), Jun. 2020, pp. 1–5. doi: 10.1109/SAM48682.2020.9104256. `[link] <https://doi.org/10.1109/SAM48682.2020.9104256>`_
+
+[ruizComparison2021] S. Ruiz, T. Dietzen, T. van Waterschoot, and M. Moonen, “A comparison between overlap-save and weighted overlap-add filter banks for multi-channel Wiener filter based noise reduction,” in 2021 29th European Signal Processing Conference (EUSIPCO), Aug. 2021, pp. 336–340. doi: 10.23919/EUSIPCO54536.2021.9616352. `[link] <https://doi.org/10.23919/EUSIPCO54536.2021.9616352>`_
+
+[uenoDirectionally2021] N. Ueno, S. Koyama, and H. Saruwatari, “Directionally weighted wave field estimation exploiting prior information on source direction,” IEEE Transactions on Signal Processing, vol. 69, pp. 2383–2395, Apr. 2021, doi: 10.1109/TSP.2021.3070228. `[link] <https://doi.org/10.1109/TSP.2021.3070228>`_
+
+[uenoKernel2018] N. Ueno, S. Koyama, and H. Saruwatari, “Kernel ridge regression with constraint of Helmholtz equation for sound field interpolation,” in 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC), Tokyo, Japan: IEEE, Sep. 2018, pp. 436–440. doi: 10.1109/IWAENC.2018.8521334. `[link] <https://doi.org/10.1109/IWAENC.2018.8521334>`_
+