BSSEvalImages(true_sources_list, estimated_sources_list, source_labels=None, algorithm_name=None, do_mono=False, compute_permutation=True)¶
Wrapper class for
mir_evalimplementation of the BSS-Eval Iamges metrics (SDR, SIR, SAR). Contains logic for loading ground truth AudioSignals and estimated AudioSignals to compute BSS-Eval Images metrics. The
mir_evalmodule contains an implementation of BSS-Eval version 3.
The BSS-Eval metrics attempt to measure perceptual quality by comparing sources estimated from a source separation algorithm to the ground truth, known sources. These metrics evaluate the distortion (SDR) and artifacts (SAR) present in the estimated signals as well as the interference (SIR) from other sources in a given estimated source. Results are returned in units of dB, with higher values indicating better quality.
- For more information on
mir_eval(python implementation of BSS-Eval v3) see
- For more information on the BSS-Eval metrics, see the webpage for
- Implementations of this base class:
musevalversion 4 BSS-Eval implementation.
- Emmanuel Vincent, Rémi Gribonval, Cédric Févotte. Performance measurement in blind
audio source separation. IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2006, 14 (4), pp.1462–1469. <inria-00544230> * Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, and Daniel P. W. Ellis, “mir_eval: A Transparent Implementation of Common MIR Metrics”, Proceedings of the 15th International Conference on Music Information Retrieval, 2014.
- true_sources_list (list) – List of
AudioSignalobjects that contain the ground truth sources for the mixture.
- estimated_sources_list (list) – List of
AudioSignalobjects that contain estimate sources, output from source separation algorithms.
- source_labels (list) – List of strings that are labels for each source to be used as keys for
the scores. Default value is
Noneand in that case labels are
Source 1, etc.
- algorithm_name (str) – Name of the algorithm if using this object to compute many BSS-Eval metrics. Can be changed later.
- do_mono (bool) – Should flatten the audio to mono before calculating metrics.
- compute_permutation (bool) – Should try to find the best permutation for the estimated sources.
Prepare the audio_data in the sources for
mir_eval. :returns: (
np.ndarray) reference_source_array, estimated_source_array
Formats and populates the
evaluate(). :param bss_output: Direct output from the
musevalfunction. :type bss_output: tuple
Returns: (dict) Reformatted dictionary from
- For more information on