结合区分性训练深度神经网络的歌声与伴奏分离方法

A separation method of singing and accompaniment combining discriminative training deep neural network

针对音乐信号中的歌声与伴奏相互关联难以分离的问题,提出了一种区分性训练深度神经网络(Deep Neural Network,DNN)的音乐分离方法。首先,在DNN模型的基础上同时考虑歌声与伴奏间的重建误差和区分性信息,提出了一种改进的目标函数进行区分性训练;其次,在DNN模型上额外添加一层,引入时频掩蔽对估计出的歌声伴奏进行联合优化,相应的时域信号由傅里叶逆变换获得;最后,验证不同参数设置对分离性能的影响,并与现有的音乐分离方法进行对比.实验结果表明,改进的目标函数和时频掩蔽的引入明显提高了DNN的分离性能,且与现有的音乐分离方法相比分离性能最高提高了4 dB从而证实所提方法是一种有效的音乐分离方法。

For the difficulty of separation between singing and accompaniment in the musical signals, an improved music separation method of based on discriminative training Depth Neural Network(DNN) was proposed. Firstly,based on the DNN model, considering the reconstruction errors and discrimination information between singing and accompaniment, an improved objective function was presented to discriminate the training;Then, an additional layer was added to DNN model, introducing the time-frequency masking to optimize the estimated accompaniment of the song, and the corresponding time-domain signal was obtained by inverse Fourier transform;Finally, the influence of different parameters on the separation performance was verified, and compared it with the existing music separation methods. The experimental results showed that the improved objective function and the introduction of time-frequency masking significantly improved the separation performance of the DNN, and the separation performance was improved about 4 dB compared with other existing music separation methods, thus verifying that the proposed method was an effective music separation algorithm.