import numpy as np
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def build_LFR_features(data, m, n):
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"""
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Actually, this implements stacking frames and skipping frames.
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if m = 1 and n = 1, just return the origin features.
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if m = 1 and n > 1, it works like skipping.
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if m > 1 and n = 1, it works like stacking but only support right frames.
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if m > 1 and n > 1, it works like LFR.
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Args:
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inputs_batch: inputs is T x D np.ndarray
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m: number of frames to stack
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n: number of frames to skip
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"""
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LFR_inputs = []
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T = data.shape[0]
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T_lfr = int(np.ceil(T / n))
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for i in range(T_lfr):
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if m <= T - i * n:
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LFR_inputs.append(np.hstack(data[i*n:i*n+m]))
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else:
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num_padding = m - (T - i * n)
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frame = np.hstack(data[i*n:])
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for _ in range(num_padding):
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frame = np.hstack((frame, data[-1]))
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LFR_inputs.append(frame)
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return np.vstack(LFR_inputs)
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