dc.contributor.author | Singh, S | |
dc.contributor.author | Bear, H | |
dc.contributor.author | Benetos, E | |
dc.contributor.author | IEEE International Conference on Acoustics, Speech and Signal Processing | |
dc.date.accessioned | 2021-02-19T15:58:08Z | |
dc.date.available | 2021-01-30 | |
dc.date.available | 2021-02-19T15:58:08Z | |
dc.date.issued | 2021-06-06 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/70431 | |
dc.description.abstract | Acoustic Scene Classification (ASC) refers to the task of assigning a semantic label to an audio stream that characterizes the environment in which it was recorded. In recent times, Deep Neural Networks (DNNs) have emerged as the model of choice for ASC. However, in real world scenarios, domain adaptation remains a persistent problem for ASC models. In the search for an optimal solution to the said problem, we explore a metric learning approach called prototypical networks using the TUT Urban Acoustic Scenes dataset, which consists of 10 different acoustic scenes recorded across 10 cities. In order to replicate the domain adaptation scenario, we divide the dataset into source domain data consisting of data samples from eight randomly selected cities and target domain data consisting of data from the remaining two cities. We evaluate the performance of the network against a selected baseline network under various experimental scenarios and based on the results we conclude that metric learning is a promising approach towards addressing the domain adaptation problem in ASC. | en_US |
dc.format.extent | ? - ? (5) | |
dc.publisher | IEEE | en_US |
dc.subject | metric learning | en_US |
dc.subject | domain adaptation | en_US |
dc.subject | acoustic scene classification | en_US |
dc.subject | episodic training | en_US |
dc.title | Prototypical Networks for Domain Adaptation in Acoustic Scene Classification | en_US |
dc.type | Conference Proceeding | en_US |
dc.rights.holder | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
pubs.author-url | http://www.eecs.qmul.ac.uk/profiles/singhshubhr.html | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
pubs.publisher-url | https://2021.ieeeicassp.org/ | en_US |
dcterms.dateAccepted | 2021-01-30 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | UKRI Centre for Doctoral Training in Artificial Intelligence and Music::Engineering and Physical Sciences Research Council | en_US |