treble_tsdk.results.base_ir

Base impulse response classes and utilities.

Provides the BaseIR class for working with impulse response data, which is the base class for MonoIR, SpatialIR and DeviceIR. It contains the basic functionality for working with impulse response data, such as filtering, resampling, and convolution.

Classes

BaseIR

Base class for impulse response handling

EnergyDrop

A class representing the energy drop of an EDC curve, in dB
class treble_tsdk.results.base_ir.BaseIR

Base class for impulse response handling

__init__(data: numpy.ndarray, sampling_rate: float, time: numpy.ndarray | None = None, source: list[Source] | None = None, receiver: list[Receiver] | None = None, zero_pad_samples: int = 0, normalization_coefficient: float | None = None)

A Class handling all mono impulse response related actions

Parameters:

  • data (np.ndarray) – The time domain impulse response

  • sampling_rate (float) – The sampling rate of the impulse response

  • source (Source | None) – Information on source, defaults to None

  • receiver (Receiver | None) – Information on receiver, defaults to None

  • zero_pad_samples (int) – The number of samples that have been zero padded in the beginning, defaults to 0

  • normalization_coefficient (float | None) – The coefficient used to normalize the data

convolve_with_audio_signal(audio: AudioSignal) → ConvolvedAudioSignal

Convolve the impulse response with an audio signal to get a convolved audio signal

Parameters:

audio (AudioSignal) – signal to be convolved with the IR

Return AudioSignal:

The convolved audio signal

filter(filter_definition: FilterDefinition | list[FilterDefinition] | Callable[[np.ndarray, int], np.ndarray] | list[Callable[[np.ndarray, int], np.ndarray]]) → MonoIR | SpatialIR | DeviceIR | AudioSignal | ConvolvedAudioSignal
Parameters:

filter_definition – The filter definition to use. Either a class inheriting from FilterDefinition, or a callable that accepts (numpy.ndarray, int) and returns a numpy.ndarray. A single value or a list of either type is accepted.

Returns:

The filtered impulse response.

classmethod from_file(filepath: str | Path, channel: int = None, zero_pad_samples: int = 0) → AudioSignal

Create from a wav file

Parameters:

  • filepath (str | Path) – path to the wav file

  • channel (int) – if the input is multichannel, choose the channel to load, defaults to None

Return AudioSignal:

the audio signal loaded from file

playback(channel: int | None = None) → IPython.display.Audio

Play back the audio using the ipython display module

Parameters:

channel (int | None) – select the channel you want to play back, if None is selected stereo will be played back if there are two channels, defaults to None

Return ipd.Audio:

IPython audio object

resample(requested_sampling_rate: float = 32000) → MonoIR | SpatialIR | DeviceIR | AudioSignal | ConvolvedAudioSignal

Resample the data to a new sampling rate

Parameters:

requested_sampling_rate (int) – The new sampling rate

Return “MonoIR | SpatialIR | DeviceIR | AudioSignal | ConvolvedAudioSignal”:

The returned audio signal

strip_first_samples(n_samples: int) → numpy.ndarray

Returns an array with the first n_samples removed

unpadded_data() → numpy.ndarray

Returns the impulse response without zero padding

Return np.ndarray:

The impulse response without zero padding

unpadded_time() → numpy.ndarray

Returns the time vector without zero padding

Return np.ndarray:

The time vector without zero padding

write_to_file(file_path: str | Path) → None

Write the impulse response to an HDF5 file

Parameters:

file_path (str | Path) – The path to the HDF5 file

write_to_wav(path_to_file: str | Path, normalize: bool = True, unpad_data: bool = True) → float

Write the data out to a .wav file

Parameters:

  • path_to_file (str | Path) – Where to store the file (the directory needs to exist)

  • normalize (bool) – Whether to normalize the data before writing to .wav if the impulse response object contains a normalization coefficient this one will be used otherwise it will be the absolute maximum of the data times two, defaults to True

  • unpad_data (bool) – Whether to unpad the data before writing to .wav, defaults to True

Return float:

The normalization coefficient used

property frequency: numpy.ndarray

Frequency vector for the frequency domain representation.

Returns np.ndarray:

Frequency values in Hz.

property frequency_response: numpy.ndarray

Frequency domain representation of the impulse response.

Returns np.ndarray:

Complex frequency response.

class treble_tsdk.results.base_ir.EnergyDrop

A class representing the energy drop of an EDC curve, in dB

__init__(start: float, end: float) → None
end: float
start: float