class das.kapre.filterbank.Filterbank(*args, **kwargs)[source]#

### Filterbank

`kapre.filterbank.Filterbank(n_fbs, trainable_fb, sr=None, init=’mel’, fmin=0., fmax=None,

bins_per_octave=12, image_data_format=’default’, **kwargs)`

#### Notes

Input/output are 2D image format. E.g., if channel_first,

  • input_shape: (None, n_ch, n_freqs, n_time)

  • output_shape: (None, n_ch, n_mels, n_time)

#### Parameters * n_fbs: int

  • Number of filterbanks

  • sr: int
    • sampling rate. It is used to initialize freq_to_mel.

  • init: str
    • if 'mel', init with mel center frequencies and stds.

  • fmin: float
    • min frequency of filterbanks.

    • If init == 'log', fmin should be > 0. Use None if you got no idea.

  • fmax: float
    • max frequency of filterbanks.

    • If init == 'log', fmax is ignored.

  • trainable_fb: bool,
    • Whether the filterbanks are trainable or not.

TODO: is sr necessary? is fmax necessary? init with None?


Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).


input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).


This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state in __init__(), or the build() method that is called automatically before call() executes the first time.

  • inputs

    Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

    arguments, and inputs cannot be provided via the default value of a keyword argument.

    • NumPy array or Python scalar values in inputs get cast as tensors.

    • Keras mask metadata is only collected from inputs.

    • Layers are built (build(input_shape) method) using shape info from inputs only.

    • input_spec compatibility is only checked against inputs.

    • Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

    • The SavedModel input specification is generated using inputs only.

    • Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

  • *args – Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.

  • **kwargs

    Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

    whether the call is meant for training or inference.

    • mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).


A tensor or list/tuple of tensors.


Computes the output shape of the layer.

This method will cause the layer’s state to be built, if that has not happened before. This requires that the layer will later be used with inputs that match the input shape provided here.


input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.


An input shape tuple.


Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.


Python dictionary.