Overview

The librosa package is structured as collection of submodules:

• librosa

  • librosa.beat

    Functions for estimating tempo and detecting beat events.

  • librosa.core

    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.

  • librosa.decompose

    Functions for harmonic-percussive source separation (HPSS) and generic spectrogram decomposition using matrix decomposition methods implemented in scikit-learn.

  • librosa.display

    Visualization and display routines using matplotlib.

  • librosa.effects

    Time-domain audio processing, such as pitch shifting and time stretching. This submodule also provides time-domain wrappers for the decompose submodule.

  • librosa.feature

    Feature extraction and manipulation. This includes low-level feature extraction, such as chromagrams, pseudo-constant-Q (log-frequency) transforms, Mel spectrogram, MFCC, and tuning estimation. Also provided are feature manipulation methods, such as delta features, memory embedding, and event-synchronous feature alignment.

  • librosa.filters

    Filter-bank generation (chroma, pseudo-CQT, CQT, etc.). These are primarily internal functions used by other parts of librosa.

  • librosa.onset

    Onset detection and onset strength computation.

  • librosa.output

    Text- and wav-file output.

  • librosa.segment

    Functions useful for structural segmentation, such as recurrence matrix construction, time-lag representation, and sequentially constrained clustering.

  • librosa.util

    Helper utilities (normalization, padding, centering, etc.)

Quickstart

Before diving into the details, we’ll walk through a brief example program