如何处理不同音频文件的 MFCC 功能差异

Question

librosa.feature.mfcc returns 不同音频文件的不同尺寸。那么如何处理这种情况以训练或测试模型

#test.py
import os
import pickle
import numpy as np 
from scipy.io.wavfile import read
import librosa as mfcc
from sklearn import preprocessing
import warnings
warnings.filterwarnings("ignore")
def get_MFCC(sr,audio):
    features = mfcc.feature.mfcc(audio,sr,n_mfcc=20, dct_type=2)
    feat     = np.asarray(())
    for i in range(features.shape[0]):
        temp = features[i,:]
        if np.isnan(np.min(temp)):
            continue
        else:
            if feat.size == 0:
                feat = temp
            else:
                feat = np.vstack((feat, temp))
    features = feat;
    features = preprocessing.scale(features)
    return features
#path to test data
source   = "C:\Users\PrashuGupta\Downloads\datasets\pygender\test_data\AudioSet\female_clips\"
#path to save trained model
modelpath     = "C:\Users\Prashu Gupta\Downloads\datasets\pygender\"


gmm_files = [os.path.join(modelpath,fname) for fname in
              os.listdir(modelpath) if fname.endswith('.gmm')]
models    = [pickle.load(open(fname,'rb')) for fname in gmm_files]
genders   = [fname.split("\")[-1].split(".gmm")[0] for fname
              in gmm_files]
files     = [os.path.join(source,f) for f in os.listdir(source)
              if f.endswith(".wav")] 
for f in files:
    print (f.split("\")[-1])
    audio,sr  = mfcc.load(f, sr = 16000,mono = True)     
    features   = get_MFCC(sr,audio)
    scores     = None
    log_likelihood = np.zeros(len(models))
    for i in range(len(models)):
        gmm    = models[i]         #checking with each model one by one
        scores = np.array(gmm.score(features))
        log_likelihood[i] = scores.sum()
    winner = np.argmax(log_likelihood)
    print ("\tdetected as - ", genders[winner],"\n\tscores:female",log_likelihood[0],",male ", log_likelihood[1],"\n")

错误

Expected the input data X have 1800 features, but got 313 features in scores = np.array(gmm.score(features))

Answer 1

要么你必须 truncate/pad 文件，使它们都具有相同的大小（比如 5 秒），要么将文件的特征汇总到一个不依赖于剪辑长度的固定长度向量中（average/min/max)，或者让分类器在固定长度特征流上运行 windows（比如 1 秒）。

如何处理不同音频文件的 MFCC 功能差异

How to handle difference in MFCC feature for difference audio file

python-3.x

librosa