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| import io | |
| import os | |
| import librosa | |
| import librosa.display | |
| import numpy as np | |
| import matplotlib | |
| from matplotlib.font_manager import fontManager | |
| import matplotlib.pyplot as plt | |
| from scipy.signal import butter, lfilter | |
| from PIL import Image | |
| FILTER_UPPER_BOUND = 20000 | |
| FILTER_LOWER_BOUND = 0 | |
| # use ./fonts/NotoSansTC-Regular.ttf | |
| fontManager.addfont("fonts/NotoSansTC-Regular.ttf") | |
| matplotlib.rc("font", family="Noto Sans TC") | |
| def butter_filter(data: np.ndarray, cutoff: int, fs: int, btype: str, order=5): | |
| nyquist = 0.5 * fs | |
| if btype in ["low", "high"]: | |
| normal_cutoff = cutoff / nyquist | |
| else: # 'band' | |
| normal_cutoff = [c / nyquist for c in cutoff] | |
| b, a = butter(order, normal_cutoff, btype=btype, analog=False) | |
| y = lfilter(b, a, data) | |
| return y | |
| def plt_to_numpy(plt: plt.Figure) -> np.ndarray: | |
| buf = io.BytesIO() | |
| plt.savefig(buf, format="png") | |
| buf.seek(0) | |
| return np.array(Image.open(buf)) | |
| def apply_filters( | |
| y: np.ndarray, | |
| sr: int, | |
| highpass_cutoff: int, | |
| lowpass_cutoff: int, | |
| bandpass_low: int, | |
| bandpass_high: int, | |
| ): | |
| if highpass_cutoff > FILTER_LOWER_BOUND: | |
| y = butter_filter(y, highpass_cutoff, sr, "high") | |
| if lowpass_cutoff > FILTER_LOWER_BOUND and lowpass_cutoff < sr / 2: | |
| y = butter_filter(y, lowpass_cutoff, sr, "low") | |
| if bandpass_low > FILTER_LOWER_BOUND and bandpass_high < sr / 2: | |
| y = butter_filter(y, [bandpass_low, bandpass_high], sr, "band") | |
| return y | |
| def analyze_audio( | |
| file: str, | |
| highpass_cutoff: int, | |
| lowpass_cutoff: int, | |
| bandpass_low: int, | |
| bandpass_high: int, | |
| ): | |
| filename = os.path.basename(file) | |
| y, sr = librosa.load(file) | |
| y = apply_filters( | |
| y, sr, highpass_cutoff, lowpass_cutoff, bandpass_low, bandpass_high | |
| ) | |
| def plot_waveform(y: np.ndarray, sr: int) -> np.ndarray: | |
| plt.figure(figsize=(14, 5)) | |
| librosa.display.waveshow(y, sr=sr) | |
| plt.title(f"Waveform ({filename})") | |
| plt.xlabel("Time") | |
| plt.ylabel("Amplitude") | |
| return plt_to_numpy(plt) | |
| def plot_spectrogram(y: np.ndarray, sr: int) -> np.ndarray: | |
| plt.figure(figsize=(14, 5)) | |
| D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max) | |
| librosa.display.specshow(D, sr=sr, x_axis="time", y_axis="log") | |
| plt.colorbar(format="%+2.0f dB") | |
| plt.title(f"Spectrogram ({filename})") | |
| return plt_to_numpy(plt) | |
| def plot_mfcc(y: np.ndarray, sr: int) -> np.ndarray: | |
| mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13) | |
| plt.figure(figsize=(14, 5)) | |
| librosa.display.specshow(mfccs, sr=sr, x_axis="time") | |
| plt.colorbar() | |
| plt.title(f"MFCC ({filename})") | |
| return plt_to_numpy(plt) | |
| def plot_zcr(y: np.ndarray) -> np.ndarray: | |
| zcr = librosa.feature.zero_crossing_rate(y=y) | |
| plt.figure(figsize=(14, 5)) | |
| plt.plot(zcr[0]) | |
| plt.title(f"Zero Crossing Rate ({filename})") | |
| plt.xlabel("Frames") | |
| plt.ylabel("Rate") | |
| return plt_to_numpy(plt) | |
| def plot_spectral_centroid(y: np.ndarray, sr: int) -> np.ndarray: | |
| spectral_centroids = librosa.feature.spectral_centroid(y=y, sr=sr)[0] | |
| frames = range(len(spectral_centroids)) | |
| t = librosa.frames_to_time(frames) | |
| plt.figure(figsize=(14, 5)) | |
| plt.semilogy(t, spectral_centroids, label="Spectral centroid") | |
| plt.title(f"Spectral Centroid ({filename})") | |
| plt.xlabel("Time") | |
| plt.ylabel("Hz") | |
| return plt_to_numpy(plt) | |
| def plot_spectral_bandwidth(y: np.ndarray, sr: int) -> np.ndarray: | |
| spectral_bandwidth = librosa.feature.spectral_bandwidth(y=y, sr=sr)[0] | |
| frames = range(len(spectral_bandwidth)) | |
| t = librosa.frames_to_time(frames) | |
| plt.figure(figsize=(14, 5)) | |
| plt.semilogy(t, spectral_bandwidth, label="Spectral bandwidth") | |
| plt.title(f"Spectral Bandwidth ({filename})") | |
| plt.xlabel("Time") | |
| plt.ylabel("Hz") | |
| return plt_to_numpy(plt) | |
| def plot_rms(y: np.ndarray) -> np.ndarray: | |
| rms = librosa.feature.rms(y=y)[0] | |
| plt.figure(figsize=(14, 5)) | |
| plt.plot(rms) | |
| plt.title(f"RMS Energy ({filename})") | |
| plt.xlabel("Frames") | |
| plt.ylabel("RMS") | |
| return plt_to_numpy(plt) | |
| def plot_spectral_contrast(y: np.ndarray, sr: int) -> np.ndarray: | |
| spectral_contrast = librosa.feature.spectral_contrast(y=y, sr=sr) | |
| plt.figure(figsize=(14, 5)) | |
| librosa.display.specshow(spectral_contrast, sr=sr, x_axis="time") | |
| plt.colorbar() | |
| plt.title(f"Spectral Contrast ({filename})") | |
| return plt_to_numpy(plt) | |
| def plot_spectral_rolloff(y: np.ndarray, sr: int) -> np.ndarray: | |
| spectral_rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)[0] | |
| frames = range(len(spectral_rolloff)) | |
| t = librosa.frames_to_time(frames) | |
| plt.figure(figsize=(14, 5)) | |
| plt.semilogy(t, spectral_rolloff, label="Spectral rolloff") | |
| plt.xlabel("Time") | |
| plt.ylabel("Hz") | |
| plt.title(f"Spectral Rolloff ({filename})") | |
| return plt_to_numpy(plt) | |
| def plot_tempo(onset_env: np.ndarray, sr: int) -> np.ndarray: | |
| dtempo = librosa.feature.tempo(onset_envelope=onset_env, sr=sr, aggregate=None) | |
| frames = range(len(dtempo)) | |
| t = librosa.frames_to_time(frames, sr=sr) | |
| plt.figure(figsize=(14, 5)) | |
| plt.plot(t, dtempo, label="Tempo") | |
| plt.title(f"Tempo ({filename})") | |
| plt.xlabel("Time") | |
| plt.ylabel("Tempo") | |
| return plt_to_numpy(plt) | |
| def plot_tempogram(onset_env: np.ndarray, sr: int) -> np.ndarray: | |
| tempogram = librosa.feature.tempogram(onset_envelope=onset_env, sr=sr) | |
| plt.figure(figsize=(14, 5)) | |
| librosa.display.specshow(tempogram, sr=sr, x_axis="time") | |
| plt.colorbar() | |
| plt.title(f"Tempogram ({filename})") | |
| return plt_to_numpy(plt) | |
| waveform = plot_waveform(y, sr) | |
| spectrogram = plot_spectrogram(y, sr) | |
| mfcc = plot_mfcc(y, sr) | |
| zcr = plot_zcr(y) | |
| spectral_centroid = plot_spectral_centroid(y, sr) | |
| spectral_bandwidth = plot_spectral_bandwidth(y, sr) | |
| rms = plot_rms(y) | |
| spectral_contrast = plot_spectral_contrast(y, sr) | |
| spectral_rolloff = plot_spectral_rolloff(y, sr) | |
| onset_env = librosa.onset.onset_strength(y=y, sr=sr) | |
| tempo = plot_tempo(onset_env, sr) | |
| tempogram = plot_tempogram(onset_env, sr) | |
| return ( | |
| waveform, | |
| spectrogram, | |
| mfcc, | |
| zcr, | |
| spectral_centroid, | |
| spectral_bandwidth, | |
| rms, | |
| spectral_contrast, | |
| spectral_rolloff, | |
| tempo, | |
| tempogram, | |
| ) | |