Name
Abstract | ||
|---|---|---|
The majority of sound scene analysis work focuses on one of two clearly defined tasks: acoustic scene classification or sound event detection. Whilst this separation of tasks is useful for problem definition, they inherently ignore some subtleties of the real-world, in particular how humans vary in how they describe a scene. Some will describe the weather and features within it, others will use a holistic descriptor like `park', and others still will use unique identifiers such as cities or names. In this paper, we undertake the task of automatic city classification to ask whether we can recognize a city from a set of sound scenes? In this problem each city has recordings from multiple scenes. We test a series of methods for this novel task and show that a simple convolutional neural network (CNN) can achieve accuracy of 50%. This is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge on the same data. A simple adaptation to the class labels of pairing city labels with grouped scenes, accuracy increases to 52%, closer to the simpler scene classification task. Finally we also formulate the problem in a multi-task learning framework and achieve an accuracy of 56%, outperforming the aforementioned approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WASPAA.2019.8937271 | 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) |
Keywords | Field | DocType |
Acoustic scene classification,location identification,city classification,computational sound scene analysis | Ask price,Scene analysis,Convolutional neural network,Computer science,Artificial intelligence,Sound event detection,Unique identifier,Machine learning | Journal |
ISSN | ISBN | Citations |
1931-1168 | 978-1-7281-1124-7 | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helen L. Bear | 1 | 30 | 7.10 |
| Toni Heittola | 2 | 366 | 25.84 |
| Annamaria Mesaros | 3 | 315 | 21.71 |
| Emmanouil Benetos | 4 | 557 | 52.48 |
| Virtanen Tuomas | 5 | 1883 | 136.57 |