语音频谱特征提取（python）

注：FFT（快速傅里叶变换）；DFT（离散傅里叶变换）；DCT（离散余弦变换）；VMD（变分模态分解）

音频文件读取：

import torchaudio
def open_audio(audio_file):  # Load an audio file.
    sig, sr = torchaudio.load(audio_file)  # 加载音频文件
    return sig, sr  # 音频时间序列， 音频采样率

通道数调整：

import torch
def re_channel(aud, new_channel):  # 统一音频通道数
    sig, sr = aud  # aud=（音频时间序列， 音频采样率）
    if sig.shape[0] == new_channel:  # 保持通道数
        return aud   # Nothing to do
    if new_channel == 1:  # 转换成单通道
        resign = sig[:1, :]  # Convert from stereo to mono by selecting only the first channel
    else:　　# 转换成多通道
        resign = torch.cat([sig, sig])  # Convert from mono to stereo by duplicating the first channel
    return resign, sr

采样率调整：

import torch
import torchaudio
def resample(aud, new_sr):  # 标准化采样率
    sig, sr = aud
    if sr == new_sr:  # 若采样率与初始相同，则不需重新采样
        return aud  # Nothing to do
    num_channels = sig.shape[0]  # 获取原通道个数
    resign = torchaudio.transforms.Resample(sr, new_sr)(sig[:1, :])  # Resample first channel
    if num_channels > 1:
        # Resample the second channel and merge both channels
        re_two = torchaudio.transforms.Resample(sr, new_sr)(sig[1:, :])
        resign = torch.cat([resign, re_two])
    return resign, new_sr

音频长度调整：

import torchaudio
import random
def pad_trunc(aud, max_ms):  # 调整为相同长度
    sig, sr = aud
    num_rows, sig_len = sig.shape
    max_len = sr // 1000 * max_ms
    if sig_len > max_len:  # 裁剪多余部分
        sig = sig[:, :max_len]  # Truncate the signal to the given length
    elif sig_len < max_len:  # 全零补充
        # Length of padding to add at the beginning and end of the signal
        pad_begin_len = random.randint(0, max_len - sig_len)
        pad_end_len = max_len - sig_len - pad_begin_len
        pad_begin = torch.zeros((num_rows, pad_begin_len))  # Pad with 0s
        pad_end = torch.zeros((num_rows, pad_end_len))
        sig = torch.cat((pad_begin, sig, pad_end), 1)  # inputs,dim=1
    return sig, sr

获取谱图特征：

from torchaudio import transforms
def spectro_gram(aud, feature, n_mel=64, n_fft=1024, hop_len=None):  # 谱图
    sig, sr = aud
    top_db = 80
    feature_choice = feature  # 谱图；Mel谱图；MFCC
    # spec has shape [channel, n_mel, time], where channel is mono, stereo etc
    # 音频信号的采样率，win_length窗口大小(默认：n_fft，FFT 的大小)，STFT 窗口之间的跳跃长度(默认：win_length // 2)，梅尔滤波器组的数量
    # MelSpectrogram返回了一个函数名，故后面加了函数需要输入的值
    if feature_choice == 'Spectrogram':
        spec = transforms.Spectrogram(n_fft=n_fft, hop_length=hop_len)(sig)
        # Convert to decibels
        spec = transforms.AmplitudeToDB(top_db=top_db)(spec)
    elif feature_choice == 'MelSpectrogram':
        spec = transforms.MelSpectrogram(sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mel)(sig)
        # Convert to decibels
        spec = transforms.AmplitudeToDB(top_db=top_db)(spec)
    else:
        spec = transforms.MFCC(sr, melkwargs={"n_fft": n_fft, "hop_length": hop_len, "n_mels": n_mel})(sig)
    return spec

获取3D谱图特征（谱图及其一阶差分和二阶差分）：

import numpy as np
import torch
def delta_delta(spector, h):
    right = np.concatenate([spector[:, 0].reshape((h, -1)), spector], axis=1)[:, :-1]
    delta = (spector - right)[:, 1:]
    delta_pad = delta[:, 0].reshape((h, -1))
    delta = np.concatenate([delta_pad, delta], axis=1)
    return delta
def get_3d_spec(spector, moments=None):  #spector：谱图
    if moments is not None:
        (base_mean, base_std, delta_mean, delta_std, delta2_mean, delta2_std) = moments
    else:
        base_mean, delta_mean, delta2_mean = (0, 0, 0)
        base_std, delta_std, delta2_std = (1, 1, 1)
    h, w = spector.shape
    delta = delta_delta(spector, h)
    delta2 = delta_delta(delta, h)
    base = (spector - base_mean) / base_std
    delta = (delta - delta_mean) / delta_std
    delta2 = (delta2 - delta2_mean) / delta2_std
    stacked = [arr.reshape((h, w, 1)) for arr in (base, delta, delta2)]
    return torch.from_numpy(np.concatenate(stacked, axis=2))

注：一阶差分就是离散函数中连续相邻两项之差【定义X(k),则Y(k)=X(k+1)-X(k)就是此函数的一阶差分，即当前语音帧与前一帧之间的关系， 体现帧与帧（相邻两帧）之间的联系】
　　二阶差分表示的是一阶差分与一阶差分之间的关系【在一阶差分的基础上，Z(k)=Y(k+1)-Y(k)=X(k+2)-2*X(k+1)+X(k)为此函数的二阶差分，即前一阶差分与后一阶差分之间的关系，体现到帧上就是相邻三帧之间的动态关系】

绘制音频波形：

import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
# 显示语音时域波形
time = np.arange(0, len(dur_aud[0][0])) * (1.0 / dur_aud[1])  #dur_aud=（音频时间序列， 音频采样率）
plt.plot(time, dur_aud[0][0])
plt.title("语音信号时域波形", fontproperties='Microsoft YaHei')
plt.xlabel("时长（秒）", fontproperties='SimHei')
plt.ylabel("振幅", fontproperties='SimHei')
plt.savefig("./img_data/语音信号时域波形图", dpi=600)
plt.show()