基于高分辨率网络的单声道歌声分离

单声道歌声分离是指将单声道歌曲中的伴奏和歌声分离，在旋律提取、歌词识别、卡拉OK伴奏等方面有重要应用。针对当前时频谱图预测精度受限的问题，利用高分辨率网络具有并行结构及特征充分交互提高模型性能的优势，提出基于高分辨率网络的单声道歌声分离算法。设计并构建适合单声道歌声分离的高分辨率网络，输入歌曲的时频谱图到网络，得到预测的伴奏和歌声时频谱图。结合歌曲相位进行重构，得到伴奏和歌声的时域信号。实验表明，在公开数据集MIR-1K上，所提算法的SNR、SIR、SAR指标均优于当前代表性算法，提高了分离后伴奏和歌声的质量。 

Monaural singing voice separation based on high-resolution network

Monaural singing voice separation separates singing voice and accompaniment from a song, which can be used for applications such as melody extraction, lyrics recognition, karaoke, etc. To resolve the limited accuracy of predicted spectrogram, this paper proposes a monaural singing voice separation algorithm based on high-resolution neural network, which has the advantages of parallel structure and sufficient features interaction for improving the performance of the model. Firstly, the high-resolution network suitable for singing voice separation is designed and constructed. Then, the spectrogram of the origin song is input to the network in order to get the predicted spectrograms of accompaniment and singing voice. Finally, the time-domain signals are reconstructed by combining the song phases with the separated spectrograms. Experiments conducted on the MIR-1K dataset show that SNR, SIR and SAR indicators of the proposed algorithm are better than those of the state-of-the-art algorithm, and the proposed algorithm improves the quality of the separated accompaniment and singing voice.