audio_processing Namespace Reference

Functions
def	window_sumsquare (window, n_frames, hop_length=200, win_length=800, n_fft=800, dtype=np.float32, norm=None)
def	griffin_lim (magnitudes, stft_fn, n_iters=30)
def	dynamic_range_compression (x, C=1, clip_val=1e-5)
def	dynamic_range_decompression (x, C=1)

Function Documentation

window_sumsquare()

def audio_processing.window_sumsquare	(		window,
			n_frames,
			hop_length = 200,
			win_length = 800,
			n_fft = 800,
			dtype = np.float32,
			norm = None
	)

# from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.

This is used to estimate modulation effects induced by windowing
observations in short-time fourier transforms.

Parameters
----------
window : string, tuple, number, callable, or list-like
    Window specification, as in `get_window`

n_frames : int > 0
    The number of analysis frames

hop_length : int > 0
    The number of samples to advance between frames

win_length : [optional]
    The length of the window function.  By default, this matches `n_fft`.

n_fft : int > 0
    The length of each analysis frame.

dtype : np.dtype
    The data type of the output

Returns
-------
wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
    The sum-squared envelope of the window function

griffin_lim()

def audio_processing.griffin_lim	(		magnitudes,
			stft_fn,
			n_iters = 30
	)

PARAMS
------
magnitudes: spectrogram magnitudes
stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods

dynamic_range_compression()

def audio_processing.dynamic_range_compression	(		x,
			C = 1,
			clip_val = 1e-5
	)

PARAMS
------
C: compression factor

dynamic_range_decompression()

def audio_processing.dynamic_range_decompression	(		x,
			C = 1
	)

PARAMS
------
C: compression factor used to compress