add chapter05後半#74
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i14kwmr
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Aug 8, 2023
| plt.plot(a[L - S :]) | ||
| plt.xlim([0, 0.01 * fs]) | ||
| plt.legend(["obs,(x1)", "enh"]) | ||
| plt.show() |
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ちなみにですが、以下のコードを記述するとSNRの比較ができます！参考までに
ポイントは信号をSTFT->iSTFTして信号長を揃えているところです（ビームフォーミング処理以外は同じ）
- s1: [STFT] -> [iSTFT]
- x1: [STFT] -> [iSTFT]
- a: [STFT] -> [ビームフォーミング] -> [iSTFT]
S1 = stft(s1, L, S, wnd)
_x1 = istft(S, X1)
_s1 = istft(S, S1)
print("input SNR:", 10 * np.log10(np.sum(_s1 ** 2) / np.sum((_x1 - _s1) ** 2))) # input SNR: 10.00
print("output SNR:", 10 * np.log10(np.sum(_s1 ** 2) / np.sum((a - _s1) ** 2))) # output SNR: 14.68
i14kwmr
reviewed
Aug 8, 2023
| psi[_f, theta] = np.conjugate(w[_f]) @ a | ||
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| plt.pcolormesh(deg, f, 20 * np.log10(abs(psi))) | ||
| plt.colorbar() |
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出力されるビームパターンを見ると、暗い色が緑 (-40くらい？)で最小値の-120のような青の部分はほとんどない印象を受けました！現状、色は黄色 ([0, -20]のあたり)に集中しているので、もう少しカラーバーの範囲 (plt.clim)を狭めると細かい調査ができて良いかと感じました！
i14kwmr
reviewed
Aug 8, 2023
| S = 512 | ||
| d = 0.05 | ||
| fs = 16000 | ||
| p = np.array([[-d, 0, 0], [0, 0, 0], [d, 0, 0]]) |
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ここはq01.pyでやっているように
pm = np.array([(m - (M - 1) // 2) * d, 0, 0])のように書くとスマートだと思います！
i14kwmr
reviewed
Aug 8, 2023
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| x = np.zeros((3, len(y))) | ||
| for i in range(len(x)): | ||
| x[i] = np.roll(y, i * 10) + white_noise |
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