TeamDr.-Ing. Anil Nagathil
Ruhr-Universität Bochum
Institut für Kommunikationsakustik
Fakultät für Elektrotechnik und Informationstechnik
Universitätsstr. 150
D-44780 Bochum
Raum: ID/2/223
Email: Anil.Nagathil@rub.de
Tel.: +49 234 32 29289
Anil Nagathil studied electrical engineering and information technology at Ruhr-Universität Bochum (RUB) and the University of Birmingham, UK. He obtained the Dipl.-Ing. and Dr.-Ing. degrees from Ruhr-Universität Bochum in 2009 and 2016, respectively. In 2007 he was a research intern at Deutsche Telekom Laboratories, Berlin. From 2016 to 2023, he was a postdoctoral researcher at the Institute of Communication Acoustics at RUB, where he currently works as a senior research scientist. His research interests include statistical speech and audio signal processing and machine learning for applications in hearing instruments.
Publications
Book Chapters:
Vatolkin, I., & Nagathil, A. (2019). Evaluation of Audio Feature Groups for the Prediction of Arousal and Valence in Music. In N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, & M. Vichi (Eds.), Studies in Classification, Data Analysis, and Knowledge Organization. Applications in Statistical Computing (Vol. 28, pp. 305–326). Springer International Publishing. (https://doi.org/10.1007/978-3-030-25147-5_19)
Martin R., Nagathil A. (2016). "Digital Filters and Spectral Analysis", in Weihs, C., Jannach, D., Vatolkin, I., Rudolph, G. (eds.): "Music Data Analysis: Foundations and Applications", CRC Press, pp. 111-143, 2016
Nagathil, A., Martin, R. (2016). "Signal-Level Features", in Weihs, C., Jannach, D., Vatolkin, I., Rudolph, G. (eds.): "Music Data Analysis: Foundations and Applications", CRC Press, pp. 145-164, 2016
Journals:
Nagathil, A. & Bruce, I. C. (2023). WaveNet-based approximation of a cochlear filtering and hair cell transduction model. The Journal of the Acoustical Society of America, 154(1), 191–202. https://doi.org/10.1121/10.0020068
Gauer, J., Nagathil, A., Lentz, B., Völter, C. & Martin, R. (2023). A subjective evaluation of different music preprocessing approaches in cochlear implant listeners. The Journal of the Acoustical Society of America, 153(2), 1307-1318. https://doi.org/10.1121/10.0017249
Martin, R., Buyens, W., Nagathil, A., Nogueira, W., van Dijk, B. & Wouters, J. (2023). COMMENT ON "NOVEL WEB-BASED MUSIC RE-ENGINEERING SOFTWARE FOR ENHANCEMENT OF MUSIC ENJOYMENT AMONG COCHLEAR IMPLANTEES" BY HWA ET AL. (OTOL NEUROTOL 2021;42(9). Otology & neurotology, 44 (1), 96–97. https://doi.org/10.1097/MAO.0000000000003752
Gauer, J., Nagathil, A., Eckel, K., Belomestny, D., & Martin, R. (2022). A versatile deep-neural-network-based music preprocessing and remixing scheme for cochlear implant listeners. The Journal of the Acoustical Society of America, 151(5), 2975-2986. (https://doi.org/10.1121/10.0010371)
Gauer, J., Nagathil, A., Martin, R., Thomas, J. P., & Völter, C. (2019). Interactive Evaluation of a Music Preprocessing Scheme for Cochlear Implants Based on Spectral Complexity Reduction. Frontiers in Neuroscience, 13, 1206. (https://doi.org/10.3389/fnins.2019.01206)
Nogueira, W., Nagathil, A., Martin, R. (2019). "Making Music More Accessible for Cochlear Implant Listeners: Recent Developments", IEEE Signal Processing Magazine, vol. 36, no. 1, pp. 115-127, January 2019.
Nagathil, A., Schlattmann, J.-W., Neumann, K., Martin, R. (2018). "Music Complexity Prediction for Cochlear Implant Listeners Based on a Feature-based Linear Regression Model", J. Acous. Soc. Am. (JASA), 144(1), pp. 1-10, July 2018.
Nagathil, A., Weihs, C., Neumann, K., Martin, R. (2017). "Spectral Complexity Reduction of Music Signals Based on Frequency-domain Reduced-rank Approximations: An Evaluation with Cochlear Implant Listeners," J. Acous. Soc. Am. (JASA), 142(3), pp. 1219-1228, September 2017.
Nagathil, A., Weihs, C., Martin, R. (2016). “Spectral Complexity Reduction of Music Signals for Mitigating Effects of Cochlear Hearing Loss,” IEEE/ACM Trans. Audio, Speech, and Language Processing, vol. 24, no. 3, pp. 445-458, March 2016.
Gergen, S., Nagathil, A., Martin, R. (2015). "Classification of reverberant audio signals using clustered ad hoc distributed microphones". Signal Processing, 107, pp. 21-32, February 2015
Conference Proceedings (peer-reviewed):
Nagathil, A., Göbel, F., Nelus, A., Bruce, I.C. (2021). “Computationally Efficient DNN-based Approximation of an Auditory Model for Applications in Speech Processing,” in Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Toronto, Canada, June 2021
Lentz, B., Nagathil, A., Gauer, J., Martin, R. (2020). “Harmonic/Percussive Sound Separation and Spectral Complexity Reduction of Music Signals for Cochlear Implant Listeners”, in Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 8713-8717, Barcelona, Spain, May 2020 (Virtual Conference)
Nagathil, A., Martin, R. (2019). "Objective Evaluation of Ideal
Time-Frequency Masking for Music Complexity Reduction in Cochlear
Implants," in Proc. International Symposium on Computer Music
Multidisciplinary Research (CMMR), Marseille, France.
Lentz, B., Nagathil, A., Gauer, J., and Martin, R. (2019). “Music Simplification for Cochlear Implant Users through Harmonic/Percussive Sound Separation and Spectral Complexity Reduction,” In: 22. Jahrestagung Der Deutschen Gesellschaft Für Audiologie, p. 1 (Abstract).
Gauer, J., Nagathil, A., Martin, R. (2018). "Binaural spectral complexity reduction of music signals for cochlear implant listeners", in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Calgary, Canada, April 2018.
Nagathil, A., Schlattmann, J.-W., Neumann, K., Martin, R. (2017). "A Feature-based Linear Regression Model for Predicting Perceptual Ratings of Music by Cochlear Implant Listeners," in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, USA, March 2017.
Krymova, E., Nagathil, A., Belomestny, D., Martin, R. (2017). "Segmentation of Music Signals Based on Explained Variance Ratio for Applications in Spectral Complexity Reduction," in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, USA, March 2017.
Gergen, S., Nagathil, A., Martin, R. (2015). "Reduction of reverberation effects in the MFCC modulation spectrum for improved classification of acoustic signals", Interspeech 2015, Dresden, September 2015.
Nagathil, A., Martin, R. (2013). "Evaluation of Spectral Transforms for Music Signal Analysis," in Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, New York, USA, October 2013.
Gergen, S., Nagathil, A., Martin, R. (2013). "Audio Signal Classification in Reverberant Environments Based on Fuzzy-Clustered Ad-hoc Microphone Arrays", International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2013, Vancouver, Canada, May 2013.
Vatolkin I., Nagathil A., Theimer W., Martin R. (2013). “Performance of Specific vs. Generic Feature Sets in Polyphonic Music Instrument Recognition,” in Proc. 7th Int. Conf. on Evolutionary Multi-Criterion Optimization (EMO), Sheffield, UK, March 2013.
Nagathil, A., Martin, R. (2012). "Optimal Signal Reconstruction from a Constant-Q Spectrum", ICASSP, pp. 349-352, Kyoto, Japan, March 2012.
Nagathil, A., Göttel, P., Martin, R. (2011). “Hierarchical Audio Classification Using Cepstral Modulation Ratio Regressions Based on Legendre Polynomials,” in Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, May 2011
Nagathil, A., Vatolkin, I., Theimer, W. (2010). “Comparison of Partition-Based Audio Features for Music Classification,” in Proc. 9. ITG-Fachtagung Sprachkommunikation, Bochum, Germany, October 2010
Nagathil, A., Gerkmann, T., Martin, R. (2010). “Musical Genre Classification Based on a Highly-resolved Cepstral Modulation Spectrum,” in Proc. European Signal Processing Conference (EUSIPCO), Aalborg, Denmark, August 2010
Martin, R., Nagathil, A. (2009). “Cepstral Modulation Ratio Regression (CMRARE) Parameters for Audio Signal Analysis and Classification,” in Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei, Taiwan, April 2009
Mauler, D., Nagathil, A., Martin, R. (2007). "On Optimal Estimation of Compressed Speech for Hearing Aids," in Proc. Interspeech, Antwerp, Belgium, August 2007
Other Conference Proceedings:
Neudek, D., Nagathil, A., Wißmann, A., Getzmann, S., Martin, R. (2021). "On Least-squares-based Auditory Attention Decoding with Individual Neural Latency Compensation", in Proc. Deutsche Jahrestagung für Akustik (DAGA), 2021.
Neudek, D., Nagathil, A., Getzmann, S., Martin, R. (2020). "Speaker Change Detection Based on Event-Related Potentials with a Consumer Brain-Computer Interface", in Proc. Deutsche Jahrestagung für Akustik (DAGA), 2020
Gauer, J., Nagathil, A., Martin, R., Thomas J.-P., Völter, C. (2018). "Validation of a Music Enhancement Method for CI Listeners Using an Interactive User Interface", (Extended Abstract) Proc. International Workshop on Speech Processing for Voice, Speech, and Hearing Disorders, Mysore, India, Sept. 8-9, 2018.
Nagathil, A., Weihs C., Martin R. (2015). “Signal Processing Strategies for Improving Music Perception in the Presence of a Cochlear Hearing Loss,” in Proc. Jahrestagung der Deutschen Gesellschaft für Audiologie (DGA), Bochum, Germany, ISBN 978-3-9813141-5-1.
Nagathil, A., Gerkmann, T., Martin, R. (2010). “Cepstral Modulation Features for Classifying Audio Data,” in Proc. 36. Deutsche Jahrestagung für Akustik (DAGA), Berlin, Germany, March 2010
Abstracts and Talks/Posters:
Nagathil, A., & Bruce, I. C. (2024). Approximation of an auditory model for outer and inner hair cell hearing impairments using a conditional WaveNet. International Hearing Aid Conference (IHCON), Tahoe City, CA, USA, August 21-24 2024.
Nagathil, A., Bruce, I.C. (2024). "A WaveNet-based approximation of a cochlear filtering and hair cell transduction model for applications in speech and music processing," Erlanger Kolloquium, Erlangen, Germany, February 2024.
Nagathil, A., Gauer, J., Jeyachandran, S., Martin, R. (2023). "Individualized optimization of a music remixing method for cochlear implant users," Conference on Implantable Auditory Prostheses (CIAP), Lake Tahoe, USA, July 2023
Nagathil, A., Bruce, I.C. (2023). "A WaveNet-based cochlear filtering and hair cell transduction model for applications in speech and music processing," 4th Virtual Conference on Computational Audiology (VCCA), June 2023.
Nagathil, A., Bruce, I.C. (2022). “A Deep-learning-based Approximation of a Cochlear Filtering Auditory Model for Applications in Speech and Music Processing,” Music and Hearing Health Workshop, Oldenburg, Germany, October 2022
Nagathil, A., Schlattmann, J.-W., Neumann, K., Martin, R. (2017). “Evaluation of Spectral Music Complexity Reduction Methods for Cochlear Implant Listeners by Means of a Perceptual Music Quality Prediction Model”, Conference on Implantable Auditory Prostheses (CIAP), Lake Tahoe, USA, July 2017
Nagathil, A., Weihs C., Neumann, K., Martin, R. (2016). “Frequency-domain Reduced-rank Approximations of Music Signals for the Improvement of Music Perception in Cochlear Implant Listeners,” ARCHES Meeting/ICanHear Conference, Zurich, Switzerland, 21.-23. November 2016
Nagathil, A., Martin, R. (2014). “Investigation into Properties of Spectral Transforms with Respect to Music Signal Analysis,” Erlanger Kolloquium, Erlangen, Germany, 28. Februar 2014
Nagathil, A., Martin, R. (2012). “Towards Timbre-controlled Reconstruction of Music Signals in the CQT Domain,“ 11th Workshop on Quality Improvement Methods, Dortmund, Germany, 8. Juni 2012
Nagathil, A., Martin, R. (2011). ”Cepstral Modulation Features for Versatile Audio Classification Tasks,” Joint Annual Conference of the German Association for Pattern Recognition (DAGM) and the German Classification Society (GfKL), Franfurt, Germany, 1. September 2011
Nagathil, A., Martin, R. (2010). ”Statistical Properties of Cepstral Modulation Features for Audio Signal Analysis,” 2nd Joint Statistical Meeting Deutsche Arbeitsgemeinschaft Statistik (DAGStat), Dortmund, Germany, March 2010
