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Librosa audio processing tutorial
2022-07-06 06:56:00 【andrew P】
Speech feature extraction : Understand Mel spectrogram (Mel-spectrogram)、 Mel inverse frequency coefficient (MFCCs) Principle _BeichenLiu.Polaris The blog of -CSDN Blog _ Mel spectrum
1.librosa.core.load(path,sr) Resample the audio
librosa.core.load — librosa 0.7.2 documentation
librosa .core. load ( route , sr=22050, Mono = really , Offset =0.0, The duration of the = nothing , dtype =<class 'numpy.float32'>, res_type='kaiser_best') [ resources ]¶
Load audio file When floating point Between sequences .
The audio will be automatically resampled to a given rate ( Default sr=22050).
To preserve the native sampling rate of the file , Please use sr=None.
2.librosa.stft(), The short-time Fourier transform
librosa.core.stft — librosa 0.7.2 documentation
The short-time Fourier transform ( STFT ).[1]( The first 2 Chapter )
STFT By calculating the discrete Fourier transform on a short overlapping window (DFT) To represent the signal in the time-frequency domain .
This function returns a complex valued matrix D bring
np.abs(D[f, t]) yes frame t Local frequency bin f Range of , also
np.angle(D[f, t]) It's the frequency bin f In frame t The aspect of .
Integers t and f You can use utility functions frames_to_sample And convert to physical units fft_frequencies.
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