fakufaku
commented
2 years ago Hello, there is not function for that in pyroomacoustics yet. There are two possibilities.
- Add many point sources. With the reverberation, the effect should be similar to diffuse noise.
- Use a diffuse noise generator such as the one from Habets et al. link to MATLAB code.
You could try to use the following python code based on Habets et al. paper. I would like to add it to the package, but I am not sure yet how.
def generate_diffuse_noise(
mic_array,
n_samples=None,
noise_sample=None,
n_fft=2048,
hop=512,
fs=16000,
c=343,
):
"""
Generates spherical diffuse noise according to the method described in
Habets et al., Generating nonstationary multisensor signals under a spatial coherence constraint, JASA, 2008.
Parameters
----------
mic_array: (n_mics, n_dim)
The locations of the sensors (in meters)
n_samples: int
The length of the target signal
noise_sample: numpy.ndarray
A template noise signal
n_fft: int
The length of the fft in the STFT
hop: int
The shift of the STFT
fs: int
The sampling frequency (in Hertz)
c: float
The speed of sound (in meters/second)
Returns
-------
The diffuse noise signal with shape (n_samples, n_mics)
"""
if noise_sample is None and n_samples is None:
raise ValueError(
"One of the desired length `n_samples` or a noise sample `noise_sample` must be provided"
)
elif n_samples is None:
n_samples = noise_sample.shape[0]
if noise_sample.ndim > 1:
noise_sample = noise_sample[:, 0]
n_mics = mic_array.shape[0]
# STFT params
win_a = pra.hamming(n_fft)
win_s = pra.transform.compute_synthesis_window(win_a, hop)
# compute the coherence matrix
coh = compute_coherence_theory(mic_array, fs, c, n_fft)
eigval, eigvec = np.linalg.eigh(coh)
eigval = np.maximum(eigval, 0.0)
shaping_matrix = eigvec * np.sqrt(eigval[:, None, :])
# generate the sensor noise (n_frames, n_freq, n_chan)
shape = ((n_samples + n_fft) // hop, n_fft // 2 + 1, n_mics)
N = np.random.randn(*shape) + 1j * np.random.randn(*shape)
if noise_sample is not None:
A = pra.transform.stft.analysis(noise_sample, n_fft, hop, win=win_a)
l_max = min(N.shape[0], A.shape[0])
N[:l_max, :, :] *= abs(A[:l_max, :, None])
diffuse_fd = np.einsum("fcd,nfd->nfc", shaping_matrix, N)
diffuse = pra.transform.stft.synthesis(diffuse_fd, n_fft, hop, win=win_s)
if noise_sample is not None:
# restore original power
std_orig = np.sqrt(np.mean(noise_sample ** 2))
std_gen = np.sqrt(np.mean(diffuse ** 2))
diffuse *= std_orig / std_gen
return diffuse[:n_samples, :]
WangRui-debug
DanTremonti
kailasa-Nischal
I'm trying to creat a room with serval speakers and a stable background noise. In this case, I hope adding diffuse noise in this room rather than adding point source noise in one position. Could you please tell me how to implement it? Thank you very much!