| 摘要 |
A sound source (101, 102, fig.1) separation method assumes that a single source predominates in each time-frequency bin thus yielding a less computationally complex binary activation model for a microphone array R in the presence of background noise. A Binary Activation Expectation Maximization (BAEM) algorithm takes source model parameters for each bin, inverts the mixture covariance matrix 402 and calculates its determinant, then computes source posterior probability 403 (equation 25 & 26), then updates the model parameters 404 (equations 14-17) in order to maximize expectation (equations 27-29) yielding Short Time Fourier Transform (STFT) coefficients (equation 30). Compared to Sub-Source Expectation Maximization (SSEM), the inversion of Rj,f need only be calculated once for each time-frequency bin. |
