Name
Title | ||
|---|---|---|
Optimization of amplitude modulation features for low-resource acoustic scene classification |
Abstract | ||
|---|---|---|
We developed a new feature extraction algorithm based on the Amplitude Modulation Spectrum (AMS), which mainly consists of two filter bank stages composed of low-order recursive filters. The passband range of each filter was optimized by using the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES). The classification task was accomplished by a Linear Discriminant Analysis (LDA) classifier. To evaluate the performance of the proposed acoustic scene classifier based on AMS features, we tested it with the publicly available dataset provided by the IEEE AASP Challenge 2013. Using only 9 optimized AMS features, we achieved 85 % classification accuracy, outperforming the best previously available approaches by 10 %. |
Year | Venue | Keywords |
|---|---|---|
2015 | European Signal Processing Conference | evolutionary optimization,acoustic scene classification,acoustic feature extraction,amplitude modulation spectrum |
Field | DocType | ISSN |
Passband,Pattern recognition,Computer science,Filter bank,Feature extraction,Evolution strategy,Artificial intelligence,CMA-ES,Amplitude modulation,Linear discriminant analysis,Classifier (linguistics) | Conference | 2076-1465 |
Citations | PageRank | References |
3 | 0.39 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Semih Agcaer | 1 | 3 | 1.07 |
| Anton Schlesinger | 2 | 3 | 0.73 |
| Falk-Martin Hoffmann | 3 | 4 | 1.45 |
| Rainer Martin | 4 | 1029 | 91.14 |