import time from Hlib import read_wav, normalize_signal, get_sinusoid, save_wav, save_model # Read the samples np.random.seed(0) max_amp = 0 names = [] signals = [] for i in range(1, 6): for j in range(1, 6): for k in range(1, 5): name = str(i) + str(j) + str(k) names.append(name) Y, _ = read_wav('samples/' + name + '.wav') Y, a = normalize_signal(Y) Y = np.array(Y + [0]) signals.append(Y) if a > max_amp: max_amp = a names = np.array(names) signals = np.array(signals) ## subsample = [0] subsample = np.array(subsample) names = names[subsample] signals = signals[subsample] ##
for j in np.linspace(1, max_dynamic2, max_dynamic2 * 2 - 1): for k in np.linspace(1, round_robins, round_robins * 2 - 1): xnames.append(str(i) + '-' + str(j) + '-' + str(k)) xipt.append([i, j, k]) xipt = np.array(xipt) xipt, maxs_xipt, mins_xipt = normalize_cols(xipt, inf_norm, sup_norm) original_len = 0 for i in range(1, max_dynamic1 + 1, 1): for j in range(1, max_dynamic2 + 1, 1): for k in range(1, round_robins + 1, 1): name = str(i) + '-' + str(j) + '-' + str(k) names.append(name) ipt.append([i, j, k]) s, fps = read_wav('samples/' + piece_name + '/' + name + '.wav') original_len = s.shape[0] max = np.max(np.abs(s)) F = rfft(s) * 2 / original_len local_max_freq = int(round(20000 / fps * original_len)) F = F[:local_max_freq] n = F.shape[0] F_real = F.real F_imag = F.imag tgt.append(np.hstack([F_real, F_imag])) tgt = np.array(tgt) M = np.max(np.abs(tgt)) tgt = tgt / M # tgt = np.cbrt(tgt)