Name
Abstract | ||
|---|---|---|
In this article we design an experimental setup to detect disturbances in voice recordings, such as additive noise, clipping, infrasound and random muting. The datasets are generated by introducing degradations into clean recordings. We test five different classification algorithms in both single- and multi-label settings: kernel substitution based support vector machine, convolutional neural netw... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414383 | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Keywords | DocType | ISBN |
Support vector machines,Hidden Markov models,Signal processing algorithms,Medical treatment,Signal processing,Classification algorithms,Reliability | Conference | 978-1-7281-7605-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helmer Nylén | 1 | 0 | 0.34 |
| Saikat Chatterjee | 2 | 24 | 5.32 |
| Sten Ternström | 3 | 2 | 1.92 |