计算机科学
光谱图
深度学习
波形
特征(语言学)
语音识别
音频信号
人工智能
噪音(视频)
音质
信号(编程语言)
特征学习
电信
语音编码
图像(数学)
雷达
语言学
哲学
程序设计语言
作者
Alberto Nogales,Santiago Donaher,Álvaro J. García‐Tejedor
标识
DOI:10.1016/j.eswa.2023.120586
摘要
People communicate daily with their mobile phones and in some cases, the quality of the communication may be vital. Thus, there is a clear interest in improving the quality of communication in cases of low signal or interferences. This paper shows how deep learning techniques are used to restore audio files that simulate situations of background noise and loss of signal. Its main distinguishing feature is the direct use of the waveform instead of a spectrogram representation which lets the model be adapted to real-time communications or broadcasting. The results show that our proposal improves performance compared to Wave-U-Net. After restoring the audio, the difference between the original and the restored audio is, on average, less than 2%. In addition, a subjective test was carried out with 113 people who detected a significant improvement in the restored audio compared to the damaged one.
科研通智能强力驱动
Strongly Powered by AbleSci AI