aknowledgments updated

gtorriani · gtorriani · commit d603966e99a7 · 2026-01-14T15:13:37.000+01:00
diff --git a/README.md b/README.md
@@ -76,7 +76,8 @@ If you would like to contribute or if you have any bugs you have found while usi
 This project is licensed under the BSD 3-Clause License. Please see [LICENSE](LICENCE) for licence guidelines.
 
 ## Aknowledgment
-The smellscape-representation methods implemented in the plotting functions of SmellscaPy are derived and adapted from the probabilistic soundscape framework described in the publication by Andrew Mitchell et al. [[1]](#ref1) and implemented in the Soundscapy open-source library (c) 2025, Andrew Mitchell All rights reserved.
+The smellscape-representation methods implemented in the plotting functions of SmellscaPy are derived and adapted from the probabilistic soundscape framework described in the publication by Andrew Mitchell et al. [[1]](#ref1) and implemented in the Soundscapy open-source library (c) 2025, Andrew Mitchell All rights reserved [[2]](#ref2) .
 We gratefully acknowledge the authors of Soundscapy for their conceptual and methodological foundation, which significantly informed the development of the smellscape visualisation tools in this project.
 
 1. <a name="ref1"></a> Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. _JASA Express Letters, 2_, 37201.
+2. <a name="ref2"></a>Mitchell, A. (2024, October). Soundscapy: A python package for soundscape assessment and analysis. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings (Vol. 270, No. 7, pp. 4029-4039). Institute of Noise Control Engineering.
diff --git a/docs/aknowledgments/index.md b/docs/aknowledgments/index.md
@@ -1,8 +1,9 @@
 <!-- markdownlint-disable MD041 -->
-The smellscape-representation methods implemented in the plotting functions of SmellscaPy are derived and adapted from the probabilistic soundscape framework described in the publication by Andrew Mitchell et al. [[1]](#ref1) and implemented in the Soundscapy open-source library (c) 2025, Andrew Mitchell All rights reserved.
+The smellscape-representation methods implemented in the plotting functions of SmellscaPy are derived and adapted from the probabilistic soundscape framework described in the publication by Andrew Mitchell et al. [[1]](#ref1) and implemented in the Soundscapy open-source library (c) 2025, Andrew Mitchell All rights reserved [[2]](#ref2).
 
 We gratefully acknowledge the authors of Soundscapy for their conceptual and methodological foundation, which significantly informed the development of the smellscape visualisation tools in this project.
 
 
 ## **References**
-1. <a name="ref1"></a> Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. _JASA Express Letters, 2_, 37201.
+1. <a name="ref1"></a> Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. _JASA Express Letters, 2_, 37201.
+2. <a name="ref2"></a> Mitchell, A. (2024, October). Soundscapy: A python package for soundscape assessment and analysis. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings (Vol. 270, No. 7, pp. 4029-4039). Institute of Noise Control Engineering.
diff --git a/docs/index.md b/docs/index.md
@@ -55,7 +55,7 @@ This project is licensed under the BSD 3-Clause License. Please see [LICENSE](li
 If you would like to contribute or if you have any bugs you have found while using `SmellscaPy', please feel free to get in touch or submit an issue or pull request!
 
 ## **Aknowledgment**
-The smellscape-representation methods implemented in the plotting functions of SmellscaPy are derived and adapted from the probabilistic soundscape framework described in the publication by Andrew Mitchell et al. [[1]](#ref1) and implemented in the Soundscapy open-source library (c) 2025, Andrew Mitchell All rights reserved.
+The smellscape-representation methods implemented in the plotting functions of SmellscaPy are derived and adapted from the probabilistic soundscape framework described in the publication by Andrew Mitchell et al. [[1]](#ref1) and implemented in the Soundscapy open-source library (c) 2025, Andrew Mitchell All rights reserved [[2]](#ref2).
 We gratefully acknowledge the authors of Soundscapy for their conceptual and methodological foundation, which significantly informed the development of the smellscape visualisation tools in this project.
 
 ## **Citation** 
@@ -65,4 +65,5 @@ Please cite us if you use the _SmellscaPy_ library:
 G. Torriani, R. Albatici, F. Babich, M. Vescovi, M. Zampini, S. Torresin, Developing a principal components model of indoor smellscape perception in office buildings, Build Environ 279 (2025) 113044. https://doi.org/10.1016/j.buildenv.2025.113044.
 
 
-1. <a name="ref1"></a> Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. _JASA Express Letters, 2_, 37201.
+1. <a name="ref1"></a> Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. _JASA Express Letters, 2_, 37201.
+2. <a name="ref2"></a> Mitchell, A. (2024, October). Soundscapy: A python package for soundscape assessment and analysis. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings (Vol. 270, No. 7, pp. 4029-4039). Institute of Noise Control Engineering.
