src.embeddings.acoustic_indices
===============================

.. py:module:: src.embeddings.acoustic_indices


Classes
-------

.. autoapisummary::

   src.embeddings.acoustic_indices.AcousticIndices


Functions
---------

.. autoapisummary::

   src.embeddings.acoustic_indices.compute_spectral_features


Module Contents
---------------

.. py:function:: compute_spectral_features(audio_file: numpy.array, sampling_rate: int, **kwargs) -> pandas.DataFrame

   Compute all spectral features for a single audio file using the `maad.features.all_spectral_features` method.

   :param audio_file: Path to the audio file to process.
   :param sampling_rate: Sampling rate of the audio file.
   :param kwargs: Additional parameters for the feature computation, such as 'nperseg', 'roi', and 'method'.
   :return: A pandas DataFrame containing all spectral features for the audio file.


.. py:class:: AcousticIndices(dataset_name: str, clip_duration: float = 3.0, model_path: Optional[Union[str, pathlib.Path]] = None, sampling_rate: Optional[int] = None)

   Bases: :py:obj:`embeddings.BaseEmbedding`


   AcousticIndices class for computing spectral features from audio data.

   This class extends BaseEmbedding and provides functionality for computing 
   various spectral features using non-machine-learning methods. It computes features
   such as peak frequency, bandwidth, skewness, etc., using the method described.

   Attributes:
   -----------
   model_path : str
       Not used in this class, but inherited from BaseEmbedding for consistency.
   dask_client : dask.distributed.client.Client or None
       Optional Dask client for handling distributed task execution.
   data : pd.DataFrame or None
       DataFrame holding the processed audio data (e.g., file paths, audio features).
   embeddings : pd.DataFrame or None
       DataFrame containing the computed acoustic features for the audio dataset.

   Methods:
   --------
   load_model():
       This method is not applicable for this class, as no model is required.

   process(dataset_name: str, extension: str = '.wav', sampling_rate: int = 48000, **kwargs):
       Processes the audio dataset to compute spectral features.

   compute_spectral_features(audio_file: str, sampling_rate: int = 48000, **kwargs) -> pd.DataFrame:
       Computes all spectral features for a single audio file using the `maad.features.all_spectral_features` method.


   .. py:method:: load_model()

      AcousticIndices does not require a machine learning model, so this method is not used.



   .. py:method:: process()

      Processes the dataset by reading the audio files and computing the acoustic indices.

      :param dataset_name: Name of the dataset to process.
      :param sampling_rate: Sampling rate for the audio files.
      :param kwargs: Additional keyword arguments for feature computation (e.g., 'nperseg', 'roi', 'method').
      :return: A pandas DataFrame containing computed acoustic features for each audio file.