input signal

length of the windowed signal after padding with zeros. The number of rows in the STFT matrix D is (1 + n_fft/2). The default value, n_fft=2048 samples, corresponds to a physical duration of 93 milliseconds at a sample rate of 22050 Hz, i.e. the default sample rate in librosa. This value is well adapted for music signals. However, in speech processing, the recommended value is 512, corresponding to 23 milliseconds at a sample rate of 22050 Hz. In any case, we recommend setting n_fft to a power of two for optimizing the speed of the fast Fourier transform (FFT) algorithm.

number of audio samples between adjacent STFT columns.

Smaller values increase the number of columns in D without affecting the frequency resolution of the STFT.

If unspecified, defaults to win_length / 4 (see below).

Each frame of audio is windowed by window() of length win_length and then padded with zeros to match n_fft.

Smaller values improve the temporal resolution of the STFT (i.e. the ability to discriminate impulses that are closely spaced in time) at the expense of frequency resolution (i.e. the ability to discriminate pure tones that are closely spaced in frequency). This effect is known as the time-frequency localization tradeoff and needs to be adjusted according to the properties of the input signal y.

If unspecified, defaults to win_length = n_fft.

Either:

• a window specification (string, tuple, or number); see scipy.signal.get_window
• a window function, such as scipy.signal.hanning
• a vector or array of length n_fft

Defaults to a raised cosine window (“hann”), which is adequate for most applications in audio signal processing.

If True, the signal y is padded so that frame D[:, t] is centered at y[t * hop_length].

If False, then D[:, t] begins at y[t * hop_length].

Defaults to True, which simplifies the alignment of D onto a time grid by means of librosa.core.frames_to_samples. Note, however, that center must be set to False when analyzing signals with librosa.stream.

Complex numeric type for D. Default is single-precision floating-point complex (np.complex64).

If center=True, this argument is passed to np.pad for padding the edges of the signal y. By default (pad_mode=”reflect”), y is padded on both sides with its own reflection, mirrored around its first and last sample respectively. If center=False, this argument is ignored.

Complex-valued matrix of short-term Fourier transform coefficients.

STFT matrix from stft

Number of frames between STFT columns. If unspecified, defaults to win_length / 4.

When reconstructing the time series, each frame is windowed and each sample is normalized by the sum of squared window according to the window function (see below).

If unspecified, defaults to n_fft.

• a window specification (string, tuple, or number); see scipy.signal.get_window
• a window function, such as scipy.signal.hanning
• a user-specified window vector of length n_fft

• If True, D is assumed to have centered frames.
• If False, D is assumed to have left-aligned frames.

Real numeric type for y. Default is 32-bit float.

If provided, the output y is zero-padded or clipped to exactly length samples.

time domain signal reconstructed from stft_matrix

An audio signal

The threshold (in decibels) below reference to consider as silence

The reference power. By default, it uses np.max and compares to the peak power in the signal.

The number of samples per analysis frame

The number of samples between analysis frames

intervals[i] == (start_i, end_i) are the start and end time (in samples) of non-silent interval i.