Librosa

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Python library used for audio manipulation especially uselful with Deep Learning

The librosa package is structured as collection of submodules:

librosa

librosa.core[edit]

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[edit]

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

librosa.beat[edit]

Functions for estimating tempo and detecting beat events.

librosa.decompose[edit]

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

librosa.display[edit]

Visualization and display routines using matplotlib.

librosa.effects[edit]

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

librosa.feature[edit]

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[edit]

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

librosa.onset[edit]

Onset detection and onset strength computation.

librosa.segment[edit]

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

librosa.sequence[edit]

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