spk-clustering.py 文件源码

def bic(arr1, arr2):
    """Bayes Information Criterion."""
    # Notes: In the seminal paper "Speakers, environment and channel
    # change detection and clustering via the Bayesian Information
    # Criterion" by Chen and Gopalakrishnan, they use a growing window
    # approach, so it's not directly comparable when using a fixed
    # sliding window.
    arr = np.concatenate((arr1, arr2))
    N1 = arr1.shape[0]
    N2 = arr2.shape[0]
    S1 = np.cov(arr1, rowvar=0)
    S2 = np.cov(arr2, rowvar=0)
    N = arr.shape[0]
    S = np.cov(arr, rowvar=0)
    d = 0.5 * N * np.log(det(S)) - 0.5 * N1 * np.log(det(S1))\
        - 0.5 * N2 * np.log(det(S2))
    p = arr.shape[1]
    corr = args.lambdac * 0.5 * (p + 0.5 * p * (p + 1)) * np.log(N)
    d -= corr
    return d