Librosa

Python library used for audio manipulation especially uselful with Deep Learning

The librosa package is structured as collection of submodules:

librosa

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.util

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

librosa.beat

Functions for estimating tempo and detecting beat events.

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, Mel spectrogram, MFCC, and various other spectral and rhythmic features. Also provided are feature manipulation methods, such as delta features and memory embedding.

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.segment

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

librosa.sequence

Functions for sequential modeling. Various forms of Viterbi decoding, and helper functions for constructing transition matrices.