recipes.cpc1.baseline package

Submodules

recipes.cpc1.baseline.compute_scores module

class recipes.cpc1.baseline.compute_scores.Model

Bases: object

Class to represent the mapping from mbstoi parameters to intelligibility scores. The mapping uses a simple logistic function scaled between 0 and 100. The mapping parameters need to fit first using mbstoi, intelligibility score pairs, using fit(). Once the fit has been made predictions can be made by calling predict()

fit(pred, intel)

Fit a mapping betweeen mbstoi scores and intelligibility scores.

params = None

predict(x)

Predict intelligilbity scores from mbstoi scores.

recipes.cpc1.baseline.compute_scores.compute_scores(predictions, labels)

recipes.cpc1.baseline.compute_scores.kt_score(x, y)

recipes.cpc1.baseline.compute_scores.ncc_score(x, y)

recipes.cpc1.baseline.compute_scores.read_data(pred_csv: Path, label_json: Path)

recipes.cpc1.baseline.compute_scores.rmse_score(x, y)

recipes.cpc1.baseline.compute_scores.run(cfg: DictConfig) → None

recipes.cpc1.baseline.compute_scores.std_err(x, y)

recipes.cpc1.baseline.run module

recipes.cpc1.baseline.run.listen(ear, signal, listener: Listener)

Generate MSBG processed signal :param ear: MSBG ear :param signal: binaural signal :param listener: listener - the listener characteristics to simulate :return: binaural signal

recipes.cpc1.baseline.run.run(cfg: DictConfig) → None

recipes.cpc1.baseline.run.run_HL_processing(cfg, path)

recipes.cpc1.baseline.run.run_calculate_SI(cfg, path) → None

Module contents