Name
Abstract | ||
|---|---|---|
In this article, we present an account of the state of the art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different implementations of its components. We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques. The data set recorded for this purpose is presented along with the performance metrics that are used to evaluate the algorithms and statistical significance tests to compare the submitted methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MSP.2014.2326181 | Signal Processing Magazine, IEEE |
Keywords | Field | DocType |
gaussian processes,acoustic signal processing,maximum likelihood estimation,mixture models,signal classification,asc techniques,acoustic scene classification,environmen classification,statistical significance test,hidden markov models,acoustics,image analysis,feature extraction,classification algorithms | Computer vision,Computer science,Implementation,Feature extraction,Speech recognition,Artificial intelligence,Hidden Markov model,Statistical classification,Machine learning,Signal processing algorithms | Journal |
Volume | Issue | ISSN |
32 | 3 | 1053-5888 |
Citations | PageRank | References |
63 | 2.86 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Barchiesi | 1 | 132 | 5.85 |
| Dimitrios Giannoulis | 2 | 301 | 14.36 |
| D. Stowell | 3 | 63 | 2.86 |
| M. D. Plumbley | 4 | 1915 | 202.38 |