Data set introduction

- EMO-DB The data set is a German emotional speech database recorded by the Berlin Institute of Technology
- 535 Audio file （ There are ten actors, five men and five women ） The content of the sentence contains the words of daily life 5 A short sentence and 5 A long sentence , Have a high degree of emotional freedom , Does not contain a particular emotional tendency . use 16kHZ sampling ,16bit quantitative , And WAV Format save file .
- Which is composed of emotions ：anger, neutral, fear, boredom, happiness, sadness, disgust.

It mainly uses this voice tag audio Of documents XXXWa.wav in W It's emotion

Extract relevant features

import feature
import os
import pickle
import tqdm
def extract_emodb(file_name:str):
    label = file_name.split('.')[0][-2]
    feature_27 = feature.extract_mfcc_feature(file_name)
    return (label,feature_27)


database_dir = "H:\dataset\EM-DB\wav"
feature_tuple =()
dir_file = os.listdir(database_dir)
for i in tqdm.tqdm(dir_file):
    file_path = os.path.join(database_dir,i)
    feature_tuple = feature_tuple+ (extract_emodb(file_path),)
with open('emo_db.pkl', 'wb+') as file:
    pickle.dump(feature_tuple,file)

Tools

def read_file(filename):
    """ input a filename and get wave data and time,framerate :param filename: :return: """
    file = wave.open(filename, 'r')
    params = file.getparams()
    nchannels, sampwidth, framerate, wav_length = params[:4]
    str_data = file.readframes(wav_length)
    wavedata = np.fromstring(str_data, dtype=np.short)

    time = np.arange(0, wav_length) * (1.0 / framerate)
    file.close()
    return wavedata, time, framerate
def extract_mfcc_feature(filename):
    """ input a filename extract mfcc from audio :param filename: :return: """
    data, time, rate = read_file(filename)
    mel_spec = ps.logfbank(data, rate, nfilt=13)
    np.set_printoptions()
    time = mel_spec.shape[0]
    if time <= 300:
        part = mel_spec
        part = np.pad(part, ((0, 300 - part.shape[0]), (0, 0)), 'constant',
                      constant_values=0)
        mfcc = dct(part)
    else:
        begin = 0
        end = 300
        part = mel_spec[begin:end, :]
        mfcc = dct(part)
    return mfcc

summary

This data set is still relatively simple , clear , I cannot understand you .