Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
An investigation of generative data augmentation for bioacoustics classification |
| Συγγραφείς: |
Herbst, Charles Daniel |
| Συνεισφορές: |
Dufourq, E., Engelbrecht, A. P., Jeantet, L., Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. |
| Στοιχεία εκδότη: |
Stellenbosch University |
| Έτος έκδοσης: |
2025 |
| Συλλογή: |
Stellenbosch University: SUNScholar Research Repository |
| Θεματικοί όροι: |
Bioacoustics -- Classification, Deep learning (Machine learning) -- Data processing, Animal sounds -- Recording and reproducing, Wildlife monitoring -- Technological innovations, UCTD |
| Περιγραφή: |
Herbst, C. D. 2025. An Investigation of Generative Data Augmentation for Bioacoustics Classification. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/04515a96-6fb4-46aa-88ad-bdd23dd377b6 ; Thesis (MEng)--Stellenbosch University, 2025. ; ENGLISH ABSTRACT: One major challenge in supervised deep learning is the need for large training datasets to achieve satisfactory generalisation performance. In the field of bioacoustics - a discipline dedicated to the recording, study and analysis of sound produced by animals - the acquisition of audio recordings from endangered animals presents a significant challenge. This is compounded by high costs, logistical constraints, and the rarity of the species in question. Typically, bioacoustics datasets have imbalanced class distribution, further complicating model training with limited examples for some rare species. To overcome this challenge, this thesis conducts and evaluation of generative models for audio augmentation. Generative models, such as variational autoencoders (VAEs) and denoising diffusion probabilistic models (DDPMs), offer the ability to create synthetic data after training on existing datasets. This thesis assesses the effectiveness of VAEs and DDPMs in augmenting various bioacoustic datasets. The datasets used include vocalisation of the critically endangered Hainan gibbon, the world's rarest primate, as well as bird calls from the pin-tailed Whydah, a resident breeding bird in South Africa, a non endangered species. The generated synthetic data was assessed through visual inspection and by computing the kernel inception distance, and compared with the distribution of the generated dataset to the training set. Furthermore, this thesis investigates the efficacy of using the generated dataset to train a deep learning classifier for identifying the Hainan gibbon calls or pin-tailed Whydah calls. For each species, two deep learning classifiers are used, namely, a self-designed convolutional ... |
| Τύπος εγγράφου: |
thesis |
| Περιγραφή αρχείου: |
xviii, 97 pages : illustrations; application/pdf |
| Γλώσσα: |
English |
| Relation: |
https://scholar.sun.ac.za/handle/10019.1/132210 |
| Διαθεσιμότητα: |
https://scholar.sun.ac.za/handle/10019.1/132210 |
| Rights: |
Stellenbosch University |
| Αριθμός Καταχώρησης: |
edsbas.DC6E6F09 |
| Βάση Δεδομένων: |
BASE |