优化这段重叠相加的代码,进行加权平均,以避免重叠部分信号失真:def overlap_add(data, frame_size, overlap): # 计算帧数 num_frames = int(len(data) / (frame_size - overlap)) # 计算每个帧的起始位置和结束位置 starts = np.arange(num_frames) * (frame_size - overlap) ends = starts + frame_size # 初始化输出数组 output = np.zeros((num_frames - 1) * overlap + frame_size) # 进行重叠相加 for i in range(num_frames): frame = data[starts[i]:ends[i]] if i == 0: output[:ends[i]] = frame else: output[starts[i]:ends[i]] += frame[overlap:] return output
时间: 2024-01-26 22:02:07 浏览: 20
可以使用加权平均的方式,对重叠部分进行平滑处理,以避免信号失真。具体来说,可以在每个帧的重叠部分,计算一个加权系数,然后对输入帧和输出帧进行加权平均。加权系数可以使用线性或者非线性的方式计算,例如可以使用三角窗、汉明窗或者三项式窗等窗函数来计算加权系数。以下是基于三角窗计算加权系数的代码示例:
```python
def overlap_add(data, frame_size, overlap):
# 计算帧数
num_frames = int(len(data) / (frame_size - overlap))
# 计算每个帧的起始位置和结束位置
starts = np.arange(num_frames) * (frame_size - overlap)
ends = starts + frame_size
# 初始化输出数组和加权系数数组
output = np.zeros((num_frames - 1) * overlap + frame_size)
weights = np.zeros_like(output)
# 计算加权系数窗口
window = np.bartlett(overlap * 2)
# 进行重叠相加
for i in range(num_frames):
frame = data[starts[i]:ends[i]]
if i == 0:
output[:ends[i]] = frame
weights[:ends[i]] = 1.0
else:
# 计算加权系数
weight = window[:overlap] + window[overlap:] * (1.0 - np.linspace(0.0, 1.0, overlap))
output[starts[i]:ends[i]-overlap] += frame[:frame_size-overlap] * weight
output[starts[i]+overlap:ends[i]] += frame[overlap:] * window[overlap:]
weights[starts[i]:ends[i]-overlap] += weight
weights[starts[i]+overlap:ends[i]] += window[overlap:]
# 对输出信号进行加权平均
output /= weights
return output
```
代码中使用了bartlett窗函数来计算加权系数窗口,然后根据重叠窗口的位置,对输入帧和输出帧进行加权平均。对于第一帧,直接将其复制到输出信号中,并将加权系数设为1.0。对于后续帧,分别计算前半部分和后半部分的加权系数,然后对输入帧和输出帧进行加权平均,并将加权系数累加到weights数组中,最后对输出信号进行加权平均。这样可以有效地避免重叠部分信号失真的问题,获得更好的重叠相加效果。