@Article{iasc.2023.035051,
AUTHOR = {Senquan Yang, Fan Ding, Jianjun Liu, Pu Li,2, Songxi Hu,2},
TITLE = {Determined Reverberant Blind Source Separation of Audio Mixing Signals},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {36},
YEAR = {2023},
NUMBER = {3},
PAGES = {3309--3323},
URL = {http://www.techscience.com/iasc/v36n3/51922},
ISSN = {2326-005X},
ABSTRACT = {Audio signal separation is an open and challenging issue in the classical “Cocktail Party Problem”. Especially in a reverberation environment, the separation of mixed signals is more difficult separated due to the influence of reverberation and echo. To solve the problem, we propose a determined reverberant blind source separation algorithm. The main innovation of the algorithm focuses on the estimation of the mixing matrix. A new cost function is built to obtain the accurate demixing matrix, which shows the gap between the prediction and the actual data. Then, the update rule of the demixing matrix is derived using Newton gradient descent method. The identity matrix is employed as the initial demixing matrix for avoiding local optima problem. Through the real-time iterative update of the demixing matrix, frequency-domain sources are obtained. Then, time-domain sources can be obtained using an inverse short-time Fourier transform. Experimental results based on a series of source separation of speech and music mixing signals demonstrate that the proposed algorithm achieves better separation performance than the state-of-the-art methods. In particular, it has much better superiority in the highly reverberant environment.},
DOI = {10.32604/iasc.2023.035051}
}