utils/UtilLibs_W.py
def delta(feat, N):
    """Compute delta features from a feature vector sequence.
    :param feat: A numpy array of size (NUMFRAMES by number of features) containing features. Each row holds 1 feature vector.
    :param N: For each frame, calculate delta features based on preceding and following N frames
    :returns: A numpy array of size (NUMFRAMES by number of features) containing delta features. Each row holds 1 delta feature vector.
    """
    if N < 1:
        raise ValueError('N must be an integer >= 1')
    NUMFRAMES = len(feat)
    denominator = 2 * sum([i**2 for i in range(1, N+1)])
    delta_feat = np.empty_like(feat)
    padded = np.pad(feat, ((N, N), (0, 0)), mode='edge')   # padded version of feat
    for t in range(NUMFRAMES):
        delta_feat[t] = np.dot(np.arange(-N, N+1), padded[t : t+2*N+1]) / denominator   # [t : t+2*N+1] == [(N+t)-N : (N+t)+N+1]
    return delta_feat
Compute delta features from a feature vector sequence.
    :param feat: A numpy array of size (NUMFRAMES by number of features) containing features. Each row holds 1 feature vector.
    :param N: For each frame, calculate delta features based on preceding and following N frames
    :returns: A numpy array of size (NUMFRAMES by number of features) containing delta features. Each row holds 1 delta feature vector.
    
Compute delta features from a feature vector sequence.
Compute delta features from a feature vector sequence .
def delta(feat, N):
    if N < 1:
        raise ValueError('N must be an integer >= 1')
    NUMFRAMES = len(feat)
    denominator = 2 * sum([i**2 for i in range(1, N+1)])
    delta_feat = np.empty_like(feat)
    padded = np.pad(feat, ((N, N), (0, 0)), mode='edge')   
    for t in range(NUMFRAMES):
        delta_feat[t] = np.dot(np.arange(-N, N+1), padded[t : t+2*N+1]) / denominator   
    return delta_feat
def delta ( feat , N ) : if N < 1 : raise ValueError ( 'N must be an integer >= 1' ) NUMFRAMES = len ( feat ) denominator = 2 * sum ( [ i ** 2 for i in range ( 1 , N + 1 ) ] ) delta_feat = np . empty_like ( feat ) padded = np . pad ( feat , ( ( N , N ) , ( 0 , 0 ) ) , mode = 'edge' ) for t in range ( NUMFRAMES ) : delta_feat [ t ] = np . dot ( np . arange ( - N , N + 1 ) , padded [ t : t + 2 * N + 1 ] ) / denominator return delta_feat
Compute delta features from a feature vector sequence.
Compute delta features from a feature vector sequence .
"""Compute delta features from a feature vector sequence.
    :param feat: A numpy array of size (NUMFRAMES by number of features) containing features. Each row holds 1 feature vector.
    :param N: For each frame, calculate delta features based on preceding and following N frames
    :returns: A numpy array of size (NUMFRAMES by number of features) containing delta features. Each row holds 1 delta feature vector.
    """ # padded version of feat # [t : t+2*N+1] == [(N+t)-N : (N+t)+N+1]
A numpy array of size (NUMFRAMES by number of features) containing delta features. Each row holds 1 delta feature vector.
A numpy array of size ( NUMFRAMES by number of features ) containing delta features . Each row holds 1 delta feature vector .
feat
A numpy array of size (NUMFRAMES by number of features) containing features. Each row holds 1 feature vector.
A numpy array of size ( NUMFRAMES by number of features ) containing features . Each row holds 1 feature vector .
N
For each frame, calculate delta features based on preceding and following N frames
For each frame calculate delta features based on preceding and following N frames