Class to blockwise convolve an arbitrary source signal with a spatial room impulse response
kernel= CArray(dtype=float, desc="Convolution kernel.")¶
Convolution kernel in the time domain. The second dimension of the kernel array has to be either 1 or match
numchannels. If only a single kernel is supplied, it is applied to all channels.
start_t= Enum(0.0, …¶
Start time of the signal in seconds, defaults to 0 s.
start= Enum(0.0, …¶
Start time of the data aquisition at microphones in seconds, defaults to 0 s.
prepadding= Enum(None, desc="Behaviour for negative time indices.")¶
Signal behaviour for negative time indices, i.e. if
start< :attr:start_t. loop take values from the end of
signal.signal()array. zeros set source signal to zero, advisable for deterministic signals. defaults to loop.
up= Enum(None, desc="upsampling factor")¶
Upsampling factor, internal use, defaults to 16.
Python generator that yields the output at microphones block-wise.
- numinteger, defaults to 128
This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block) .
- Samples in blocks of shape (num, numchannels).
The last block may be shorter than num.