| Action | Key |
|---|---|
| Play / Pause | K or space |
| Mute / Unmute | M |
| Toggle fullscreen mode | F |
| Select next subtitles | C |
| Select next audio track | A |
| Toggle automatic slides maximization | V |
| Seek 5s backward | left arrow |
| Seek 5s forward | right arrow |
| Seek 10s backward | shift + left arrow or J |
| Seek 10s forward | shift + right arrow or L |
| Seek 60s backward | control + left arrow |
| Seek 60s forward | control + right arrow |
| Seek 1 frame backward | alt + left arrow |
| Seek 1 frame forward | alt + right arrow |
| Decrease volume | shift + down arrow |
| Increase volume | shift + up arrow |
| Decrease playback rate | < |
| Increase playback rate | > |
| Seek to end | end |
| Seek to beginning | beginning |
In this talk we present a brief introduction to acoustic sensor networks and to feature extraction schemes that aim to improve the privacy vs. utility trade-off for audio classification in acoustic sensor networks. Our privacy enhancement approach consists of neural-network-based feature extraction models which aim to minimize undesired extraneous information in the feature set. To this end, we present adversarial, siamese and variational information feature extraction schemes in conjunction with neural-network-based classification (trust) and attacker (threat) models. We consider and compare schemes with explicit knowledge of the threat model and without such knowledge. For the latter, we analyze and apply the variational information approach in a smart-home scenario. It is demonstrated that the proposed privacy-preserving feature representation generalizes well to variations in dataset size and scenario complexity while successfully countering speaker identification attacks.
When subscribed to notifications, an email will be sent to you for all added annotations.
Your user account has no email address.