MIR_HW1/Q4-1-2-cqt.py at master · TWcamel/MIR_HW1 · GitHub

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#!/usr/bin/python
# -*- coding:utf-8 -*-
from glob import glob
from collections import defaultdict
import librosa
import numpy as np
from scipy.stats import pearsonr
from tqdm import tqdm
from sklearn.metrics import accuracy_score

import utils  # self-defined utils.py file

DB = 'Giantsteps'
if DB == 'GTZAN':  # dataset with genre label classify at parent directory
    FILES = glob(DB+'/wav/*/*.wav')
else:
    FILES = glob(DB+'/wav/*')

n_fft = 100  # (ms)
hop_length = 25  # (ms)

if DB == 'GTZAN':
    label, pred = defaultdict(list), defaultdict(list)
else:
    label, pred = list(), list()
chromagram = list()
gens = list()

for f in tqdm(FILES):
    f = f.replace('\\', '/')
    # print("file: ", f)
    content = utils.read_keyfile(f, '*.key')
    # print("key: ", content,"\t")
    if (len(content) < 0):
        continue  # skip saving if key not found
    if DB == 'GTZAN':
        gen = f.split('/')[2]
        label[gen].append(utils.LABEL[int(content)])
        gens.append(gen)
    else:
        label.append(content)

    sr, y = utils.read_wav(f)

    cxx = librosa.feature.chroma_cqt(y=y, sr=sr)
    chromagram.append(cxx)  # store into list for further use
    chroma_vector = np.sum(cxx, axis=1)
    # print(chroma_vector)
    key_ind = np.where(chroma_vector == np.amax(chroma_vector))
    key_ind = int(key_ind[0])
    # print('key index: ', key_ind)

    MODE = {"major": [1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1],
            "minor": [1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0]}
    MODE['major'] = utils.rotate(MODE['major'], key_ind)
    MODE['minor'] = utils.rotate(MODE['minor'], key_ind)
    r_co_major = pearsonr(chroma_vector, MODE["major"])
    r_co_minor = pearsonr(chroma_vector, MODE["minor"])
    # print(r_co_major[0])
    # print(r_co_minor[0])

    mode = ''
    if DB == 'Giantsteps':
        Cmajor_annoatation = (key_ind+3)%12
        if (r_co_major[0] > r_co_minor[0]):
            mode = Cmajor_annoatation
        else:
            mode = Cmajor_annoatation+12
        mode = utils.lerch_to_str(mode)
    else:
        if (r_co_major[0] > r_co_minor[0]):
            mode = key_ind
        else:
            mode = key_ind+12
        mode = utils.lerch_to_str(mode)
        # print('mode', mode)

    if DB == 'Giantsteps':
        pred.append(mode)
    else:
        pred.append('?')

    label_list = label
    pred_list = pred

print("***** Q4-GiantSteps *****")
if DB == 'Giantsteps':
    correct_all = 0
    for acc_len in range(len(label_list)):
        if label_list[acc_len] == pred_list[acc_len]:
            correct_all += 1
    try:
        acc_all = correct_all / len(label_list)
    except ZeroDivisionError:
        acc_all = 0
print("----------")
print("Overall accuracy:\t{:.2%}".format(acc_all))