International Audio Laboratories Erlangen (AudioLabs)

Address:
Am Wolfsmantel 33
91058 Erlangen



Subordinate Organisational Units

Juniorprofessur für Virtual Reality
Lehrstuhl für Audiocodierung (AudioLabs) (Stiftungslehrstuhl)
Lehrstuhl für Audiosignalanalyse (AudioLabs) (Stiftungslehrstuhl)
Lehrstuhl für Semantische Audiosignalverarbeitung (AudioLabs)
Professur für Sprachcodierung (AudioLabs) (Stiftungsprofessur)
Professur für wahrnehmungsbasierte räumliche Audiosignalverarbeitung (AudioLabs) (Stiftungsprofessur)


Publications (Download BibTeX)

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Schneider, M., & Habets, E. (2019). Iterative DFT-Domain Inverse Filter Optimization Using a Weighted Least-Squares Criterion. IEEE/ACM Transactions on Audio, Speech and Language Processing, 27(12), 1957-1969. https://dx.doi.org/10.1109/TASLP.2019.2936385
Müller, M., Zalkow, F., & Balke, S. (2019). Evaluating Salience Representations for Cross-Modal Retrieval of Western Classical Music Recordings. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Brighton, UK.
Dick, S., & Herre, J. (2019). Predicting the Precision of Elevation Localization Based on Head Related Transfer Functions. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 326-330). Brighton, GB: Institute of Electrical and Electronics Engineers Inc..
Stöter, F.-R., Chakrabarty, S., Edler, B., & Habets, E. (2019). CountNet: Estimating the number of concurrent speakers using supervised learning. IEEE/ACM Transactions on Audio, Speech and Language Processing, 27(2), 268--282. https://dx.doi.org/10.1109/TASLP.2018.2877892
Lopez-Serrano, P., Dittmar, C., Özer, Y., & Müller, M. (2019). NMF Toolbox: Music Processing Applications of Nonnegative Matrix Factorization. In Proceedings of the International Conference on Digital Audio Effects (DAFx). Birmingham, UK.
Müller, M., & Zalkow, F. (2019). FMP Notebooks: Educational Material for Teaching and Learning Fundamentals of Music Processing. In Proceedings of the International Conference on Music Information Retrieval (ISMIR). Delft, The Netherlands.
Abeßer, J., & Müller, M. (2019). Fundamental Frequency Contour Classification: A Comparison Between Hand-Crafted And Cnn-Based Features. In Proceedings of the {IEEE} International Conference on Acoustics, Speech, and Signal Processing ({ICASSP}). Brighton, UK.
Müller, M., Weiß, C., & Brand, F. (2019). Mid-Level Chord Transition Features for Musical Style Analysis. In Proceedings of the {IEEE} International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Brighton, UK.
Driedger, J., Schreiber, H., Haas, B., & Müller, M. (2019). Towards Automatically Correcting Tapped Beat Annotations for Music Recordings. In Proceedings of the International Conference on Music Information Retrieval ({ISMIR}). Delft, The Netherlands.
Schreiber, H., & Müller, M. (2019). Musical Tempo and Key Estimation with Directional Convolutional Neural Networks. In Proceedings of the Sound and Music Computing Conference ({SMC}). Malaga, Spain.
Weiß, C., Schlecht, S., Rosenzweig, S., & Müller, M. (2019). Towards Computational Feedback for Western Choral Singing Assessment. In Proceedings of the International Conference on Music Information Retrieval ({ISMIR}). Delft, The Netherlands.
Tänzer, M., Abeßer, J., Mimilakis, S.I., Weiß, C., Lukashevich, H.M., & Müller, M. (2019). Towards Improving CNN-Based Cross-Dataset Instrument Family Recognition In Classical Music. In Proceedings of the International Conference on Music Information Retrieval ({ISMIR}). Delft, The Netherlands.
Mimilakis, S.I., Weiß, C., Arifi-Müller, V., Abeßer, J., & Müller, M. (2019). Cross-Version Singing Voice Detection in Opera Recordings: Challenges for Supervised Learning. In Proceedings of the International Workshop on Machine Learning and Music ({MML}). Würzburg, Germany.
Chakrabarty, S., & Habets, E. (2019). Multi-speaker DOA estimation using deep convolutional networks trained with noise signals. IEEE Journal of Selected Topics in Signal Processing. https://dx.doi.org/10.1109/JSTSP.2019.2901664
Mirabilii, D., & Habets, E. (2019). Multi-channel wind noise reduction using the Corcos model. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). UK.
Schreiber, H., & Müller, M. (2018). A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network. In Proceedings of the International Conference on Music Information Retrieval (ISMIR) (pp. 98-105). Paris, France.
Rummukainen, O., Wang, J., Li, Z., Robotham, T., Yan, Z., Li, Z.,... Habets, E. (2018). Influence of visual content on the perceived audio quality in virtual reality. In Proc. Audio Eng. Soc. Convention. New York, USA: Audio Engineering Society.
Abeßer, J., Balke, S., & Müller, M. (2018). Improving Bass Saliency Estimation using Transfer Learning and Label Propagation. In Proceedings of the International Conference on Music Information Retrieval (ISMIR) (pp. 306-312). Paris, France.
Weiß, C., Balke, S., Abeßer, J., & Müller, M. (2018). Computational Corpus Analysis: A Case Study on Jazz Solos. In Proceedings of the International Conference on Music Information Retrieval (ISMIR) (pp. 416-423). Paris, France.
Haji Ghassemi, N., Hannink, J., Martindale, C., Gaßner, H., Müller, M., Klucken, J., & Eskofier, B. (2018). Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson’s Disease. Sensors.


Publications in addition (Download BibTeX)


Torcoli, M., Freke-Morin, A., Paulus, J., Simon, C., & Shirley, B. (2019). Background ducking to produce esthetically pleasing audio for TV with clear speech. In AES 146th International Convention. Dublin, IE: Audio Engineering Society.

Last updated on 2019-24-04 at 10:28