vault backup: 2023-12-12 18:22:47

.obsidian/workspace.json

@@ -53,7 +53,7 @@
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+    "attachments/Pasted image 20231212181543.png",
+    "00. Inbox/Mel spectrogram.md",
+    "attachments/Pasted image 20231212180917.png",
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00. Inbox/使用 librosa 做 FFT.md

@@ -0,0 +1,27 @@
+
+```python
+import numpy as np
+n_fft=2048
+ft = np.abs(librosa.stft(y[:n_fft], hop_length=n_fft+l))
+plt.plot(ft)
+plt.title('Spectrum')
+plt.xlabel('Frequency Bin')
+plt.ylabel('Amp1itude')
+```
+
+output e.g.:
+![[Pasted image 20231212180917.png]]
+
+這時候 Y 軸是 amplitude，可以使用 `librosa.amplitude_to_db()` 來把 amplitude 轉為 db。
+
+```python
+import librosa.display
+spec = np.abs(librosa.stft(y, hop_1ength=512))
+spec = librosa.amplitude_to_db(spec, ref=np.max)
+librosa.display.specshow(spec, sr=sr, x_axis='time', y_axis='log')
+p1t.colorbar(format='%+2.0f dB')
+plt.title( 'Spectrogram')
+```
+
+Output:
+![[Pasted image 20231212181543.png]]

00. Inbox/使用 librosa 做 mel spectrogram.md

@@ -0,0 +1,13 @@
+由於人類會對低頻低音高的片段更感興趣，所以會對通過 FFT 變換得到的 Amplitude 和 Frequency。
+[[Mel spectrogram]] 和 spectrogram 的差別就是 mel spectrogram 的頻率是 mel scale 變換後的頻率（你可以想像把Spectrogram整體往下壓）
+
+```python
+mel_spect = librosa.feature.melspectrogram(y=y, sr=sr, n fft=2048, hop_Iength=1024)
+mel_spect = librosa.power_to_db(mel_spect, ref=np.max)
+librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time')
+plt.title('Mel Spectrogram')
+p1t.colorbar(format='%+2.0f dB')
+```
+
+Output:
+![[Pasted image 20231212181946.png]]