Unsupervised Anomaly Detection in Industrial Machine Sounds Using a Multi-Student, Single-Teacher Framework with Repurposed CNN14 Embeddings

Sengar, A. (Ayush)

LIACS Thesis Repository

These webpages contain theses and reports by students affiliated with the various bachelor and master programmes offered at the Leiden Institute of Advanced Computer Science (LIACS), the computer science and artificial intelligence department of Leiden University.
Note: this thesis repository might be incomplete for certain programmes and years.

Master Computer Science

The following thesis was written by a student in the 2024-2025 class of the Master Computer Science programme at Leiden University.

Thesis details

Title	Unsupervised Anomaly Detection in Industrial Machine Sounds Using a Multi-Student, Single-Teacher Framework with Repurposed CNN14 Embeddings
Student	Sengar, A. (Ayush)
Programme	Master Computer Science
Year	2024-2025
Supervisors	Bakker, dr. E.M. (Erwin) Lew, prof.dr. M.S.K. (Michael)

Citation details

Sengar, A. (Ayush), Unsupervised Anomaly Detection in Industrial Machine Sounds Using a Multi-Student, Single-Teacher Framework with Repurposed CNN14 Embeddings, Thesis Master Computer Science, LIACS, Leiden University, 2025.