2019/10/26 by Hamidreza Baradaran Kashani, Ata Jodeiri, Kashani, Hamidreza Baradaran +5 · 1 citation
Computer Science · Engineering · #Advanced Image Processing Techniques #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Sound (cs.SD) #Speech and Audio Processing #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.12116
Accepted at 4th Conference on Technology In Electrical and Computer Engineering (ETECH 2019)
arxiv created 2019/10/26 · openalex publication_date 2019/10/26 · arxiv updated 2019/10/29 · openalex created_date 2019/11/01 · openalex updated_date 2026/07/28
Clipping, as a current nonlinear distortion, often occurs due to the limited dynamic range of audio recorders. It degrades the speech quality and intelligibility and adversely affects the performances of speech and speaker recognitions. In this paper, we focus on enhancement of clipped speech by using a fully convolutional neural network as U-Net. Motivated by the idea of image-to-image translation, we propose a declipping approach, namely U-Net declipper in which the magnitude spectrum images of clipped signals are translated to the corresponding images of clean ones. The experimental results show that the proposed approach outperforms other declipping methods in terms of both quality and intelligibility measures, especially in severe clipping cases. Moreover, the superior performance of the U-Net declipper over the well-known declipping methods is verified in additive Gaussian noise conditions.