msaf/utils.py
def lognormalize(F, floor=0.1, min_db=-80):
    """Log-normalizes features such that each vector is between min_db to 0."""
    assert min_db < 0
    F = min_max_normalize(F, floor=floor)
    F = np.abs(min_db) * np.log10(F)  # Normalize from min_db to 0
    return F
def lognormalize(F, floor=0.1, min_db=-80):
    assert min_db < 0
    F = min_max_normalize(F, floor=floor)
    F = np.abs(min_db) * np.log10(F)  # Normalize from min_db to 0
    return F
def lognormalize ( F , floor = 0.1 , min_db = - 80 ) : assert min_db < 0 F = min_max_normalize ( F , floor = floor ) F = np . abs ( min_db ) * np . log10 ( F ) # Normalize from min_db to 0 return F
Log-normalizes features such that each vector is between min_db to 0.
Log - normalizes features such that each vector is between min_db to 0 .