55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
import scipy.io.wavfile as wav
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from scipy import signal
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import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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import ipdb;
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# 读取音频文件
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filename = "./tang1.wav"
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sample_rate, sound_array = wav.read(filename)
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sound_array = sound_array.T[0, :] if sound_array.ndim != 1 else sound_array
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sound_array = sound_array / np.max(np.abs(sound_array)) # 归一化
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frame_length = int(sample_rate * 0.01)
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num_frames = len(sound_array) // frame_length
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autocorrelation = np.zeros((num_frames, frame_length))
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autocorrelation_of_candidates = np.zeros((num_frames, frame_length))
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min_peak_threshold = min(sample_rate // 400, frame_length)
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max_peak_threshold = min(sample_rate // 80, frame_length)
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for n in range(num_frames):
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frame = sound_array[n * frame_length: (n + 1) * frame_length]
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autocorrelation[n, :] = signal.correlate(frame, frame, mode='full')[frame_length - 1:]
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# 基频阈值为80-400Hz，则基音周期（即延迟）t最小为sample_rate/400，最大为sample_rate/80
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# 本应该使用峰值的延迟作为基音周期的候选值，但是发现峰值（局部最大值）并不好判断，同时一帧内的点数不多，因此将阈值内的所有点都作为候选点
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# 那么将不在阈值内的自相关系数置为一个非常小的数，从而不让算法选择不在阈值内的基音周期
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autocorrelation_of_candidates[n, :] = np.pad(
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autocorrelation[n, min_peak_threshold : max_peak_threshold],
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(min_peak_threshold, max(frame_length - max_peak_threshold, 0)),
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mode='constant',
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constant_values=-30.0,
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)
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dist = -autocorrelation
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cost = np.zeros((num_frames, frame_length))
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path = np.zeros((num_frames, frame_length))
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for n in range(num_frames - 1):
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for j in range(min_peak_threshold, max_peak_threshold):
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# f0 = sample_rate / candidate
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cost[n + 1, j] = dist[n + 1, j] + np.min(
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cost[n, :] + np.abs(sample_rate / np.arange(frame_length) - sample_rate / j)
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)
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path[n + 1, j] = np.argmin(
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cost[n, :] + np.abs(sample_rate / np.arange(frame_length) - sample_rate / j)
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)
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l_hat = np.zeros(num_frames, dtype=np.int32)
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l_hat[num_frames - 1] = np.argmin(cost[num_frames - 1, :])
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for n in range(num_frames - 2, -1, -1):
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l_hat[n] = path[n + 1, l_hat[n + 1]]
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f0 = sample_rate / l_hat
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