Matches in Ghent University Academic Bibliography for { <https://biblio.ugent.be/publication/01HHHDV5KGCR9T3KR8XS1CS0Q6> ?p ?o. }
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- 01HHHDV5KGCR9T3KR8XS1CS0Q6 classification P1.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 date "2023".
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 language "eng".
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 type conference.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 hasPart 01HHHQDDNDKPRMY3RM4BNX8YYR.pdf.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 hasPart 01HHHQE4N5VEEP350V74C4FFV7.pdf.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 subject "Technology and Engineering".
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 doi "10.21437/interspeech.2023-1021".
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 issn "2308-457X".
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 presentedAt urn:uuid:0a0d3305-b34b-46f6-ac9a-ffb0ccfb2b22.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 abstract "Sound events in daily life carry rich information about the objective world. The composition of these sounds affects the mood of people in a soundscape. Most previous approaches only focus on classifying and detecting audio events and scenes, but may ignore their perceptual quality that may impact humans' listening mood for the environment, e.g. annoyance. To this end, this paper proposes a novel hierarchical graph representation learning (HGRL) approach which links objective audio events (AE) with subjective annoyance ratings (AR) of the soundscape perceived by humans. The hierarchical graph consists of fine-grained event (fAE) embeddings with single-class event semantics, coarse-grained event (cAE) embeddings with multi-class event semantics, and AR embeddings. Experiments show the proposed HGRL successfully integrates AE with AR for AEC and ARP tasks, while coordinating the relations between cAE and fAE and further aligning the two different grains of AE information with the AR.".
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author 74b2b3d1-0782-11ec-8e0e-d61de1051915.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author F43ABB58-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author bf552237-bd78-11ea-9edd-84a31b5b5824.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author urn:uuid:0128f00a-7fc4-443b-8252-9cecbf903e81.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author urn:uuid:175073d9-19aa-47dd-8a9c-54d79da78f64.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author urn:uuid:38ff550e-588e-48af-b0b7-529c2a03d47c.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author urn:uuid:5a75af0d-c7fb-49c7-8a86-2f258c43a256.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author urn:uuid:83e94779-2d5e-4897-aad8-c512984f15e3.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author urn:uuid:ae3d566b-8235-48fc-a27d-c064fd2a560c.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 author urn:uuid:b240a121-3895-4d69-a855-7155892f7e96.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 dateCreated "2023-12-13T11:08:58Z".
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 dateModified "2024-12-12T20:50:04Z".
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 name "Joint prediction of audio event and annoyance rating in an urban soundscape by hierarchical graph representation learning".
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 pagination urn:uuid:bddbb881-c30e-41ef-84e5-6927996a9838.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 publisher urn:uuid:5a15dc56-cb26-48d0-b3df-f411eabb9ba8.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 sameAs LU-01HHHDV5KGCR9T3KR8XS1CS0Q6.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 sourceOrganization urn:uuid:04256c21-5c9f-4394-a373-9e70600e0a9d.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 sourceOrganization urn:uuid:9d847df6-be7e-48c4-bddc-84b013fe25b1.
- 01HHHDV5KGCR9T3KR8XS1CS0Q6 type P1.