Abstract
In order to personalize the behavior of hearing aid devices in different acoustic environments, we need to develop personalized acoustic scene classifiers. Since we cannot afford to burden an individual hearing aid user with the task to collect a large acoustic database, we aim instead to train a scene classifier on just one (or maximally a few) in-situ recorded acoustic waveform of a few seconds duration per scene. In this paper we develop such a”one-shot” personalized scene classifier, based on a Hidden Semi-Markov model. The presented classifier consistently outperforms a more classical Dynamic-Time-Warping-Nearest-Neighbor classifier, and correctly classifies acoustic scenes about twice as well as a (random) chance classifier after training on just one recording of 10 seconds duration per scene.
| Original language | English |
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| Title of host publication | 2018 26th European Signal Processing Conference, EUSIPCO 2018 |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 862-866 |
| Number of pages | 5 |
| Volume | 2018-September |
| ISBN (Electronic) | 9789082797015 |
| ISBN (Print) | 978-90-827970-1-5 |
| DOIs | |
| Publication status | Published - Sept 2018 |
| Event | 26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy Duration: 3 Sept 2018 → 7 Sept 2018 |
Conference
| Conference | 26th European Signal Processing Conference, EUSIPCO 2018 |
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| Abbreviated title | EUSIPCO 2018 |
| Country/Territory | Italy |
| City | Rome |
| Period | 3/09/18 → 7/09/18 |
Funding
This work is part of the research programme HearScan with project number 13925, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). We also gratefully acknowledge the developers of the pyhsmm [18] package, which was helpful to execute the inference tasks.