端点检测算法代码
时间: 2023-08-21 19:01:39 浏览: 46
以下是一种简单的基于能量的端点检测算法的Python代码:
```
import numpy as np
import librosa
def energy_based_vad(signal, sr, frame_duration=0.025, frame_shift=0.01, threshold_factor=1.5, smooth_window=10):
# 分帧
frame_length = int(frame_duration * sr)
frame_step = int(frame_shift * sr)
signal_length = len(signal)
num_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step))
pad_signal_length = num_frames * frame_step + frame_length
z = np.zeros((pad_signal_length - signal_length))
pad_signal = np.append(signal, z)
indices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + np.tile(np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T
frames = pad_signal[indices.astype(np.int32, copy=False)]
# 计算帧能量
frame_energies = np.sum(frames ** 2, axis=1)
# 平滑处理
smooth_energy = np.convolve(frame_energies, np.ones((smooth_window,)) / smooth_window, mode='same')
# 计算阈值
threshold = threshold_factor * np.median(smooth_energy)
# 确定有语音的帧
speech_frames = np.where(smooth_energy >= threshold)[0]
# 确定语音段的起始和结束帧
diff_speech_frames = np.diff(speech_frames)
start_frames = np.insert(speech_frames[1:][diff_speech_frames > 1], 0, speech_frames[0])
end_frames = np.append(speech_frames[:-1][diff_speech_frames > 1], speech_frames[-1])
# 将帧转换为时间
start_times = librosa.frames_to_time(start_frames, sr=sr, hop_length=frame_step)
end_times = librosa.frames_to_time(end_frames, sr=sr, hop_length=frame_step)
return start_times, end_times
```
该算法首先将信号分帧,并计算每一帧的能量,然后对能量进行平滑处理,并计算出一个阈值。最后,根据阈值确定有语音的帧,并确定语音段的起始和结束帧。