Alexander Lerch at his desk.

Alexander Lerch

Associate Dean of Research and Creative Practice
Professor

Contact

Email
alexander.lerch@gatech.edu
Telephone
(404) 385-7256
Office
School of Architecture 266A
Links
Music Informatics Lab
Audio Content Analysis textbook
alexanderlerch.com
Google Scholar
LinkedIn
GitHub

Alexander Lerch

Associate Dean of Research and Creative Practice
Professor

Areas of Research or Creative Practice: Music Information Retrieval, Audio Content Analysis

Education

Ph.D., Technical University of Berlin, Germany. 2004–2008
Major (summa cum laude): Audio Communication

Dipl.-Ing., Technical University of Berlin, Germany. 1994–2000
Major (summa cum laude): Electrical Engineering

Biography

Alexander Lerch is Associate Dean for Research and Creative Practice and Professor at the College of Design, Georgia Institute of Technology.

Lerch's research on machine understanding of audio and music positions him at the intersection of signal processing, machine learning, and music. He aims at creating artificially intelligent software for music generation, production, and consumption.

Lerch studied Electrical Engineering at the Berlin Institute of Technology and Tonmeister (music production) at the University of Arts, Berlin; he received his PhD (Audio Communications) from the Berlin Institute of Technology in the year 2008. He co-founded the company zplane.development, an industry leader providing advanced music technology to the music industry. zplane technologies are nowadays used by millions of musicians and producers world-wide in a wide variety of products.

Lerch has published more than 60 peer-reviewed publications on a wide range of topics in audio and music analysis and processing. His textbook "An Introduction to Audio Content Analysis" was published by Wiley/IEEE Press in 2012 (2nd edition in 2023).

Statement of Teaching Interest

Lerch teaches classes on topics such as machine learning and signal processing for audio and music, audio software engineering, and audio analysis.

Statement of Research Interest

Lerch's research on machine understanding of audio and music positions him at the intersection of signal processing, machine learning, and music. He aims at creating artificially intelligent software for music generation, production, and consumption.

 

 

Recent Scholarly Work
  1. U. Zölzer and A. Lerch, “Digitale Audio-Effekte,” in Handbuch der Audiotechnik, S. Weinzierl, Ed., Berlin, Heidelberg: Springer, 2025, pp. 1–22. doi: 10.1007/978-3-662-60357-4_28-1.
  2. K. N. Watcharasupat, Y. Ding, T. A. Ma, P. Seshadri, and A. Lerch, “Uncertainty Estimation in the Real World: A Study on Music Emotion Recognition,” in Proceedings of the European Conference on Information Retrieval (ECIR), Lucca, Italy: arXiv, 2025. doi: 10.48550/arXiv.2501.11570.
  3. J. Park et al., “Two Web Toolkits for Multimodal Piano Performance Dataset Acquisition and Fingering Annotation,” in Late Breaking Demo (Extended Abstract), Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Daejeon, South Korea, Sept. 2025. doi: 10.48550/arXiv.2509.15222.
  4. T. (Aleksandra) Ma and A. Lerch, “Singing Voice Separation using Video Input as Privileged Information during Training,” presented at the Speech and Audio in the Northeast (SANE), New York, NY, 2025.
  5. A. Lerch, C. Arthur, N. Bryan-Kinns, C. Ford, Q. Sun, and A. Vinay, “Survey on the Evaluation of Generative Models in Music,” CSUR, vol. 58, no. 4, p. 99:1-99:36, Oct. 2025, doi: 10.1145/3769106.
  6. Y. Kim et al., “PianoVAM: A Multimodal Piano Performance Dataset,” in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Daejeon, South Korea, Sept. 2025. doi: 10.48550/arXiv.2509.08800.
  7. Y. Kim, C. Han, A. Sarode, N. Posner, S. Guhathakurta, and A. Lerch, “Audio-Based Pedestrian Detection in the Presence of Vehicular Noise,” in Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), Barcelona, Spain, Sept. 2025. doi: 10.48550/arXiv.2509.19295.
  8. K. N. Watcharasupat and A. Lerch, “A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems,” in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), San Francisco, 2024. doi: 10.48550/arXiv.2406.18747.
  9. K. N. Watcharasupat and A. Lerch, “Quantifying Spatial Audio Quality Impairment,” in Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul: Institute of Electrical and Electronics Engineers (IEEE), 2024. doi: 10.48550/arXiv.2309.06531.
  10. P. Seshadri, C. Han, B.-W. Koo, N. Posner, S. Guhathakurta, and A. Lerch, “ASPED: An Audio Dataset for Detecting Pedestrians,” in Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul: Institute of Electrical and Electronics Engineers (IEEE), 2024. doi: 10.48550/arXiv.2309.06531.
  11. T. A. Ma and A. Lerch, “Music auto-tagging in the long tail: A few-shot approach,” in Proceedings of the AES Convention, New York, 2024. doi: 10.48550/arXiv.2409.07730.
  12. S. Liu and A. Lerch, “Enhancing Video Music Recommendation with Transformer-Driven Audio-Visual Embeddings,” in Proceedings of the IEEE International Symposium on the Internet of Sounds (IS2), Erlangen, 2024. doi: 10.1109/IS262782.2024.10704086.
  13. D. Li, L. Wang, L. Li, W. Guo, Q. Wu, and A. Lerch, “A Large-Scale Multiobjective Particle Swarm Optimizer With Enhanced Balance of Convergence and Diversity,” IEEE Transactions on Cybernetics, vol. 54, no. 3, pp. 1596–1607, 2024, doi: 10.1109/TCYB.2022.3225341.
  14. Y. Kim and A. Lerch, “Towards Robust Transcription: Exploring Noise Injection Strategies for Training Data Augmentation,” in Late Breaking Demo (Extended Abstract), Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), San Francisco, 2024. doi: 10.48550/arXiv.2410.14122.
  15. C. Han et al., “Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensors,” Urban Info, vol. 3, no. 1, p. 22, 2024, doi: 10.1007/s44212-024-00053-9.