src/timbre_analysis.py

import librosa
from librosa import display
from librosa import feature

import numpy as np
from numpy import typing as npt

from matplotlib import pyplot as plt
import scipy


def spectral_centroid_analysis(y: npt.ArrayLike, sr: int, shift_array: npt.ArrayLike) -> None :

    S, phase = librosa.magphase(librosa.stft(y=y))
    cent = librosa.feature.spectral_centroid(S=S)
    times = librosa.times_like(cent, sr)

    fig, ax = plt.subplots()
    librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
                             y_axis='log', x_axis='time', ax=ax, sr=sr)
    ax.plot(times, cent.T, label='Spectral centroid', color='w')
    ax.legend(loc='upper right')
    ax.set(title='log Power spectrogram')
    ax.set_xticks(shift_array - shift_array[0],
                         shift_array)
    ax.autoscale()

    result = np.vstack((times, cent))

    return fig, ax, result


def rolloff_frequency_analysis(y: npt.ArrayLike, sr: int, roll_percent:float = 0.99,
                               shift_array: npt.ArrayLike =None) -> None :

    rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr, roll_percent=roll_percent)
    rolloff_min = librosa.feature.spectral_rolloff(y=y, sr=sr, roll_percent=0.01)
    times = librosa.times_like(rolloff, sr)
    S, phase = librosa.magphase(librosa.stft(y))

    fig, ax = plt.subplots()
    librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
                             y_axis='log', x_axis='time', ax=ax, sr=sr)
    ax.plot(librosa.times_like(rolloff,sr), rolloff[0], label=f'Roll-off frequency ({roll_percent})')
    ax.plot(librosa.times_like(rolloff,sr), rolloff_min[0], color='w',
            label='Roll-off frequency (0.01)')
    ax.legend(loc='lower right')
    ax.set(title='log Power spectrogram')
    ax.set_xticks(shift_array - shift_array[0],
                         shift_array)
    ax.autoscale()

    result = np.vstack((times, rolloff, rolloff_min))

    return fig, ax, result

def spectral_bandwidth_analysis(y: npt.ArrayLike, sr: int, shift_array: npt.ArrayLike =None) -> None :
    
    S, phase = librosa.magphase(librosa.stft(y=y))
    spec_bw = librosa.feature.spectral_bandwidth(S=S)
    times = librosa.times_like(spec_bw, sr)

    fig, ax = plt.subplots(nrows=2, sharex=True)
    centroid = librosa.feature.spectral_centroid(S=S, sr=sr)
    ax[0].semilogy(times, spec_bw[0], label='Spectral bandwidth')
    ax[0].set(ylabel='Hz', xticks=[], xlim=[times.min(), times.max()])
    ax[0].legend()
    ax[0].label_outer()
    librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
                             y_axis='log', x_axis='time', ax=ax[1], sr=sr)
    ax[1].set(title='log Power spectrogram')
    ax[1].fill_between(times, np.maximum(0, centroid[0] - spec_bw[0]),
                       np.minimum(centroid[0] + spec_bw[0], sr/2),
                       alpha=0.5, label='Centroid +- bandwidth')
    ax[1].plot(times, centroid[0], label='Spectral centroid', color='w')
    ax[1].legend(loc='lower right')
    ax[1].set_xticks(shift_array - shift_array[0],
                         shift_array)
    ax[1].autoscale()

    result = np.vstack((times, spec_bw))

    return fig, ax, result


def harmonic_percussive_source_separation(y: npt.ArrayLike, sr: int,
        shift_array: npt.ArrayLike =None                                      
    ) -> None :

    D = librosa.stft(y)
    H, P = librosa.decompose.hpss(D)
    t = librosa.frames_to_time(np.arange(D.shape[1]), sr=sr)
    
    fig, ax = plt.subplots(nrows=3, sharex=False, sharey=False, figsize=(12, 8))
    # 設置子圖之間的水平間距和垂直間距
    plt.subplots_adjust(hspace=0.6, wspace=0.3)
    img = librosa.display.specshow(librosa.amplitude_to_db(np.abs(D),ref=np.max),
                                   y_axis='log', x_axis='time', ax=ax[0], sr=sr)
    ax[0].set(title='Full power spectrogram')
    #// ax[0].label_outer()
    ax[0].set_xlabel('') # 不顯示x軸名稱
    ax[0].set_xticks(shift_array - shift_array[0],
                         shift_array)
    ax[0].autoscale()

    librosa.display.specshow(librosa.amplitude_to_db(np.abs(H), ref=np.max(np.abs(D))),
                             y_axis='log', x_axis='time', ax=ax[1], sr=sr)
    ax[1].set(title='Harmonic power spectrogram')
    #// ax[1].label_outer()
    ax[1].set_xlabel('') # 不顯示x軸名稱
    ax[1].set_xticks(shift_array - shift_array[0],
                         shift_array)
    ax[1].autoscale()

    librosa.display.specshow(librosa.amplitude_to_db(np.abs(P), ref=np.max(np.abs(D))),
                             y_axis='log', x_axis='time', ax=ax[2], sr=sr)
    ax[2].set(title='Percussive power spectrogram')
    ax[2].set_xticks(shift_array - shift_array[0],
                         shift_array)
    ax[2].autoscale()
    
    fig.colorbar(img, ax=ax, format='%+2.0f dB')

    return fig, ax, (D, H, P, t)