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| Content Provider | ACM Digital Library |
|---|---|
| Author | Schuller, Björn Zhang, Zixing Pohjalainen, Jouni Fabien Ringeval, Fabien |
| Abstract | Signal noise reduction can improve the performance of machine learning systems dealing with time signals such as audio. Real-life applicability of these recognition technologies requires the system to uphold its performance level in variable, challenging conditions such as noisy environments. In this contribution, we investigate audio signal denoising methods in cepstral and log-spectral domains and compare them with common implementations of standard techniques. The different approaches are first compared generally using averaged acoustic distance metrics. They are then applied to automatic recognition of spontaneous and natural emotions under simulated smartphone-recorded noisy conditions. Emotion recognition is implemented as support vector regression for continuous-valued prediction of arousal and valence on a realistic multimodal database. In the experiments, the proposed methods are found to generally outperform standard noise reduction algorithms. |
| Starting Page | 670 |
| Ending Page | 674 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450336031 |
| DOI | 10.1145/2964284.2967306 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-10-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Noise reduction Denoising Speech emotion recognition |
| Content Type | Text |
| Resource Type | Article |
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