pyFDN.AttenuationFilter

class pyFDN.AttenuationFilter(rt, delays, fs, *, rt_nyquist=None, design='graphic_eq', rt_crossover=None, nfft=16384, alias_decay_db=0.0, device=None, dtype=None, requires_grad=True)

Parallel in-loop SOS bank parametrized by reverberation time.

For graphic_eq, rt is the ten-band target. First-order shelves and one-pole filters use rt at DC and the separately named rt_nyquist target; omitting the latter creates a flat target. Targets may additionally carry one value per delay line. The filter is implemented with FLAMO’s parallelSOSFilter because an FDN applies one SOS cascade to each delay line in parallel.

__init__(rt, delays, fs, *, rt_nyquist=None, design='graphic_eq', rt_crossover=None, nfft=16384, alias_decay_db=0.0, device=None, dtype=None, requires_grad=True)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(rt, delays, fs, *[, rt_nyquist, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
assign_value(new_value[, indx])	Assigns new values to the parameters.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
check_input_shape(x)	Checks if the dimensions of the input tensor x are compatible with the module.
check_param_shape()	Checks if the shape of the SOS parameters is valid.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
design_sos(gain_db)
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(x[, ext_param])	Applies the Filter module to the input tensor x.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_freq_convolve()	Frequency-domain matrix product with the input.
get_freq_response()	Compute the frequency response of the cascaded SOS.
get_gamma()	Calculate the value of \(\gamma\) based on the alias decay in dB and the number of FFT points.
get_io()	Computes the number of input and output channels based on the size parameter.
get_map()	Mapping for raw SOS coefficients.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_poly_coeff(param)	Split mapped parameters into b and a polynomials (parallel case), apply anti-aliasing envelope, and compute frequency response in double precision.
get_size()	Leading dimensions for SOS parameters.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
init_param()	Initialize parameters to identity sections: b=[1,0,0], a=[1,0,0].
initialize_class()	Initialize the SosFilter class.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules([remove_duplicate])	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
probe(z)	Evaluate the transfer matrix H(z) at arbitrary complex z.
probe_w(w)	Evaluate the transfer matrix H(w) at an arbitrary complex w-plane point.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
rt_to_sos(rt)
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
training

rt_to_sos(rt)

Return type:

Any