Machine learning and audio processing : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, Auckland, New Zealand
| dc.contributor.author | Ma, Junbo | |
| dc.date.accessioned | 2020-09-03T02:59:20Z | |
| dc.date.available | 2020-09-03T02:59:20Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | In this thesis, we addressed two important theoretical issues in deep neural networks and clustering, respectively. Also, we developed a new approach for polyphonic sound event detection, which is one of the most important applications in the audio processing area. The developed three novel approaches are: (i) The Large Margin Recurrent Neural Network (LMRNN), which improves the discriminative ability of original Recurrent Neural Networks by introducing a large margin term into the widely used cross-entropy loss function. The developed large margin term utilises the large margin discriminative principle as a heuristic term to navigate the convergence process during training, which fully exploits the information from data labels by considering both target category and competing categories. (ii) The Robust Multi-View Continuous Subspace Clustering (RMVCSC) approach, which performs clustering on a common view-invariant subspace learned from all views. The clustering result and the common representation subspace are simultaneously optimised by a single continuous objective function. In the objective function, a robust estimator is used to automatically clip specious inter-cluster connections while maintaining convincing intra-cluster correspondences. Thus, the developed RMVCSC can untangle heavily mixed clusters without pre-setting the number of clusters. (iii) The novel polyphonic sound event detection approach based on Relational Recurrent Neural Network (RRNN), which utilises the relational reasoning ability of RRNNs to untangle the overlapping sound events across audio recordings. Different from previous works, which mixed and packed all historical information into a single common hidden memory vector, the developed approach allows historical information to interact with each other across an audio recording, which is effective and efficient in untangling the overlapping sound events. All three approaches are tested on widely used datasets and compared with recently published works. The experimental results have demonstrated the effectiveness and efficiency of the developed approaches. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10179/15586 | |
| dc.identifier.wikidata | Q112447231 | |
| dc.identifier.wikidata-uri | https://www.wikidata.org/wiki/Q112447231 | |
| dc.language.iso | en | en_US |
| dc.publisher | Massey University | en_US |
| dc.rights | The Author | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Neural networks (Computer science) | en_US |
| dc.subject | Cluster analysis | en_US |
| dc.subject | Data processing | en_US |
| dc.subject | Computer sound processing | en_US |
| dc.subject.anzsrc | 460707 Sound and music computing | en |
| dc.title | Machine learning and audio processing : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, Auckland, New Zealand | en_US |
| dc.type | Thesis | en_US |
| massey.contributor.author | Ma, Junbo | |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |