Spectral Denoising for Microphone Classification
| Autor: | Luca Cuccovillo, Antonio Giganti, Paolo Bestagini, Patrick Aichroth, Stefano Tubaro |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) ComputingMethodologies_PATTERNRECOGNITION Audio and Speech Processing (eess.AS) Computer Science::Sound FOS: Electrical engineering electronic engineering information engineering Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing |
| Popis: | In this paper, we propose the use of denoising for microphone classification, to enable its usage for several key application domains that involve noisy conditions. We describe the proposed analysis pipeline and the baseline algorithm for microphone classification, and discuss various denoising approaches which can be applied to it within the time or spectral domain; finally, we determine the best-performing denoising procedure, and evaluate the performance of the overall, integrated approach with several SNR levels of additive input noise. As a result, the proposed method achieves an average accuracy increase of about 25% on denoised content over the reference baseline. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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