Python 基于librosa的特征提取

我正在使用从Github获得的以下代码。此代码提取MFCC、色度、光谱图、tonnetz和光谱对比度特征，并以feat.np的形式输出。我想提取一些其他特性，如rmse、zerocross，但当我添加相关代码时，在连接时会给出错误

# coding= UTF-8
#
# Author: Fing
# Date  : 2017-12-03
#

import glob
import os
import librosa
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import specgram
import soundfile as sf

def extract_feature(file_name):
    X, sample_rate = sf.read(file_name, dtype='float32')
    if X.ndim > 1:
        X = X[:,0]
    X = X.T

    # short term fourier transform
    stft = np.abs(librosa.stft(X))

    # mfcc
    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)

    # chroma
    chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)

    # melspectrogram
    mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)

    # spectral contrast
    contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)

    tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)

    return mfccs,chroma,mel,contrast,tonnetz

def parse_audio_files(parent_dir,sub_dirs,file_ext='*.wav'):
    features, labels = np.empty((0,193)), np.empty(0)
    for label, sub_dir in enumerate(sub_dirs):
        for fn in glob.glob(os.path.join(parent_dir, sub_dir, file_ext)):
            try:
                mfccs, chroma, mel, contrast,tonnetz = extract_feature(fn)
            except Exception as e:
                print("[Error] extract feature error. %s" % (e))
                continue
            ext_features = np.hstack([mfccs,chroma,mel,contrast,tonnetz])
            print(ext_features)
            print(features)
            features = np.vstack([features,ext_features])
            # labels = np.append(labels, fn.split('/')[1])
            labels = np.append(labels, label)
        print("extract %s features done" % (sub_dir))
    return np.array(features), np.array(labels, dtype = np.int)

def one_hot_encode(labels):
    n_labels = len(labels)
    n_unique_labels = len(np.unique(labels))
    one_hot_encode = np.zeros((n_labels,n_unique_labels))
    one_hot_encode[np.arange(n_labels), labels] = 1
    return one_hot_encode

# Get features and labels
r = os.listdir("data/")
r.sort()
features, labels = parse_audio_files('data', r)
np.save('feat.npy', features)
np.save('label.npy', labels)
 `

这段代码工作得很好，但当我想提取其他功能，如rmse，过零率等。当我加上

#rmse=np.mean(librosa.feature.rmse(y=X).T,axis=0)

我犯了以下错误

File "C:\Users\HP\Anaconda2\lib\site-packages\numpy\core\shape_base.py", line 237, in vstack
    return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)

ValueError: all the input array dimensions except for the concatenation axis must match exactly

---

如何提取其他功能并连接

STFT和RMSE的形状不同于MFCC和其他特征。STFT和MFCC是二维的，而其他是一维的特性。

也许你应该给出python输出来强调你的答案。如果使用原始的问题代码给出，则会更好。