【ASR】librosa处理音频文件

Librosa是一个用于音频、音乐分析、处理的python工具包。

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.core.load(path, sr=22050, mono=True, offset=0.0, duration=None, dtype=<class ‘numpy.float32’>, res_type=’kaiser_best’)

Load an audio file as a floating point time series.

Audio will be automatically resampled to the given rate (default sr=22050).

To preserve the native sampling rate of the file, use sr=None.

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.sequence

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

librosa.util

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

• librosa.util.normalize(S, norm=inf, axis=0, threshold=None, fill=None)

Normalize an array along a chosen axis.

Given a norm (described below) and a target axis, the input array is scaled so that

\[norm(S, axis=axis) == 1\]

For example, $axis=0$ normalizes each column of a 2-d array by aggregating over the rows (0-axis). Similarly, $axis=1$ normalizes each row of a 2-d array.

This function also supports thresholding small-norm slices: any slice (i.e., row or column) with norm below a specified threshold can be left un-normalized, set to all-zeros, or filled with uniform non-zero values that normalize to 1.

Note: the semantics of this function differ from $scipy.linalg.norm$ in two ways: multi-dimensional arrays are supported, but matrix-norms are not.