Dr Islah Ali-MacLachlan

• Acoustics and Audio Analysis: Expertise in environmental and musical acoustics, with a focus on computational ethnomusicology.
• Industry-Academia Partnerships: Pioneering the integration of industry partnerships into academic modules, enhancing student employability.
• Curriculum Development: Design and delivery of research-informed curricula in sound engineering, live sound, and music technology.
• Research Leadership: Leading large-scale, interdisciplinary research projects with significant funding, including work in machine learning for audio analysis.
• Student Satisfaction: Successfully managing degree programmes with consistently high satisfaction rates in the National Student Survey (NSS).
• Computational Musicology: Specialising in the use of algorithms, including machine learning, for audio detection and classification in world music.
• Conference Organisation: Leadership in organising international academic conferences, contributing to the advancement of acoustics and audio engineering.
• Professional Engagement: Active involvement in the Institute of Acoustics (IOA), contributing to the development of methodologies and policies in the field.
• Innovative Audio Engineering Teaching: Developing and implementing cutting-edge teaching practices in audio engineering, including hands-on learning with industry-standard tools and technologies.
• PhD Supervision: Mentoring and supervising PhD students, fostering the next generation of researchers in acoustics and audio analysis.

• 2019 PhD (Birmingham City University)
• 2019 PGDip Acoustics and Noise Control (IOA) (University of Derby)
• 1991 BSc (Hons) Industrial Design & Technology (Napier University of Edinburgh)

Dr. Ali-MacLachlan is a member of the following organisations:

• Institute of Acoustics (IoA)
• Audio Engineering Society (AES)

Dr. Ali-MacLachlan currently teaches on the following courses:

• BSc (Hons) Sound Engineering and Production
• BSc (Hons) Music Technology

Dr Ali-MacLachlan's research focusses on computational analysis of sound, cultural heritage, acoustics, and computational ethnomusicology.

Dr. Ali-MacLachlan is currently on the supervision team for five PhD students, including two as director of studies.

View a full list of Dr. Ali-MacLachlan's publications by visiting his Google Scholar profile.

Wilson, W., Granieri, N., & Ali-MacLachlan, I., (2023). Ubiquitous multimodality as a tool in violin performance classification. In Proceedings of the 4th International Symposium on the Internet of Sounds, Pisa, Italy, pp. 1-8.

West, B.J., Ali-MacLachlan, I., & Deuchars, A.M., (2023). An in-situ investigation of office soundscape perceptual evaluation methodologies. Acoustics 2023: Institute of Acoustics Annual Conference, Winchester, UK.

Wilson, W., Granieri, N., & Ali-MacLachlan, I., (2023). Time’s up for the Myo? - The smartwatch as an alternative for audio gestural analyses. In Proceedings of the New Interfaces for Musical Expression (NIME), Mexico City, Mexico.

Wilson, W., Ali-MacLachlan, I., & Granieri, N., (2022). Combining gestural and audio approaches to the classification of violin bow strokes. In Proceedings of the 10th International Workshop on Folk Music Analysis (FMA), Sheffield, UK.

Xiao, J., Aletta, F., & Ali-MacLachlan, I., (2022). On the opportunities of the soundscape approach to revitalise acoustics training in undergraduate architectural courses.  14(4), p.1957.

Ali-MacLachlan, I., Hunt, E., & Jamieson, A., (2020). Player recognition for traditional Irish flute recordings using K-Nearest Neighbour classification. In Proceedings of the 2nd International Conference on Timbre, Thessaloniki, Greece.

Köküer, M., Ali-MacLachlan, I., Kearney, D. and Jančovič, P., 2019. Curating and annotating a collection of traditional Irish flute recordings to facilitate stylistic analysis. International Journal on Digital Libraries, 20, pp.107-121.

Ali-MacLachlan, I., Southall, C., Tomczak, M., & Hockman, J., (2018). Player recognition for traditional Irish flute recordings. In Proceedings of the 8th International Workshop on Folk Music Analysis (FMA), Thessaloniki, Greece.

Ali-MacLachlan, I., Southall, C., Tomczak, M., & Hockman, J., (2017). Improved onset detection for traditional Irish flute recordings using convolutional neural networks. In Proceedings of the 7th International Workshop on Folk Music Analysis (FMA), Malaga, Spain.

Ali-MacLachlan, I., Tomczak, M., Southall, C., & Hockman, J., (2016). Note, cut and strike detection for traditional Irish flute recordings. In Proceedings of the 6th International Workshop on Folk Music Analysis (FMA), Dublin, Ireland.

Ali-MacLachlan, I., Köküer, M., Athwal, C., & Jancovic, P., (2015). Towards the identification of Irish traditional flute players from commercial recordings. In Proceedings of the 5th International Workshop on Folk Music Analysis (FMA), Paris, France.

Köküer, M., Jancovic, P., Ali-MacLachlan, I., & Athwal, C., (2014). Automated detection of single-and multi-note ornaments in Irish traditional flute playing. In Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR), Taipei, Taiwan, pp. 15-20.

Ali-MacLachlan, I., Köküer, M., Jancovic, P., Williams, I., & Athwal, C., (2013). Quantifying timbral variations in traditional Irish flute playing. In Proceedings of the 3rd International Workshop on Folk Music Analysis (FMA), Amsterdam, Netherlands.

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