Room impulse response reshaping-based expectation–maximization in an underdetermined reverberant environment

Source separation in an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation–maximization algorithm. However, it is limited to high reverberation environments, resulting in bad or even invalid separation performance. To eliminate this restriction, a room impulse response reshaping-based expectation–maximization method is designed to solve the problem of source separation in an underdetermined reverberant environment. Firstly, a room impulse response reshaping technology is designed to eliminate the influence of audible echo on the reverberant environment, improving the quality of the received signals. Then, a new mathematical model of time-frequency mixing signals is established to reduce the approximation error of model transformation caused by high reverberation. Furthermore, an improved expectation–maximization method is proposed for real-time update learning rules of model parameters, and then the sources are separated using the estimators provided by the improved expectation–maximization method. Experimental results based on source separation of speech and music mixtures demonstrate that the proposed algorithm achieves better separation performance while maintaining much better robustness than popular expectation–maximization methods.