The librosa package is structured as collection of submodules:
Core functionality includes functions to load audio from disk, compute various spectrogram representations, and a variety of commonly used tools for music analysis. For convenience, all functionality in this submodule is directly accessible from the top-level librosa.* namespace.
Helper utilities (normalization, padding, centering, etc.)
Functions for estimating tempo and detecting beat events.
Functions for harmonic-percussive source separation (HPSS) and generic spectrogram decomposition using matrix decomposition methods implemented in scikit-learn.
Visualization and display routines using matplotlib.
Time-domain audio processing, such as pitch shifting and time stretching. This submodule also provides time-domain wrappers for the decompose submodule.
Feature extraction and manipulation. This includes low-level feature extraction, such as chromagrams, Mel spectrogram, MFCC, and various other spectral and rhythmic features. Also provided are feature manipulation methods, such as delta features and memory embedding.
Filter-bank generation (chroma, pseudo-CQT, CQT, etc.). These are primarily internal functions used by other parts of librosa.
Onset detection and onset strength computation.
Functions useful for structural segmentation, such as recurrence matrix construction, time-lag representation, and sequentially constrained clustering.
Functions for sequential modeling. Various forms of Viterbi decoding, and helper functions for constructing transition matrices.