Caution

You're reading an old version of this documentation. If you want up-to-date information, please have a look at 0.9.1.

Tutorial

This section covers the fundamentals of developing with librosa, including a package overview, basic and advanced usage, and integration with the scikit-learn package. We will assume basic familiarity with Python and NumPy/SciPy.

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. (Deprecated)

  • 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.)

Quickstart

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

# Beat tracking example
from __future__ import print_function
import librosa

# 1. Get the file path to the included audio example
filename = librosa.util.example_audio_file()

# 2. Load the audio as a waveform `y`
#    Store the sampling rate as `sr`
y, sr = librosa.load(filename)

# 3. Run the default beat tracker
tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)

print('Estimated tempo: {:.2f} beats per minute'.format(tempo))

# 4. Convert the frame indices of beat events into timestamps
beat_times = librosa.frames_to_time(beat_frames, sr=sr)

The first step of the program:

filename = librosa.util.example_audio_file()

gets the path to the audio example file included with librosa. After this step, filename will be a string variable containing the path to the example audio file. The example is encoded in OGG Vorbis format, so you will need the appropriate codec installed for audioread.

The second step:

y, sr = librosa.load(filename)

loads and decodes the audio as a time series y, represented as a one-dimensional NumPy floating point array. The variable sr contains the sampling rate of y, that is, the number of samples per second of audio. By default, all audio is mixed to mono and resampled to 22050 Hz at load time. This behavior can be overridden by supplying additional arguments to librosa.load().

Next, we run the beat tracker:

tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)

The output of the beat tracker is an estimate of the tempo (in beats per minute), and an array of frame numbers corresponding to detected beat events.

Frames here correspond to short windows of the signal (y), each separated by hop_length = 512 samples. Since v0.3, librosa uses centered frames, so that the kth frame is centered around sample k * hop_length.

The next operation converts the frame numbers beat_frames into timings:

beat_times = librosa.frames_to_time(beat_frames, sr=sr)

Now, beat_times will be an array of timestamps (in seconds) corresponding to detected beat events.

The contents of beat_times should look something like this:

7.43
8.29
9.218
10.124
...

Advanced usage

Here we’ll cover a more advanced example, integrating harmonic-percussive separation, multiple spectral features, and beat-synchronous feature aggregation.

# Feature extraction example
import numpy as np
import librosa

# Load the example clip
y, sr = librosa.load(librosa.util.example_audio_file())

# Set the hop length; at 22050 Hz, 512 samples ~= 23ms
hop_length = 512

# Separate harmonics and percussives into two waveforms
y_harmonic, y_percussive = librosa.effects.hpss(y)

# Beat track on the percussive signal
tempo, beat_frames = librosa.beat.beat_track(y=y_percussive,
                                             sr=sr)

# Compute MFCC features from the raw signal
mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_mfcc=13)

# And the first-order differences (delta features)
mfcc_delta = librosa.feature.delta(mfcc)

# Stack and synchronize between beat events
# This time, we'll use the mean value (default) instead of median
beat_mfcc_delta = librosa.util.sync(np.vstack([mfcc, mfcc_delta]),
                                    beat_frames)

# Compute chroma features from the harmonic signal
chromagram = librosa.feature.chroma_cqt(y=y_harmonic,
                                        sr=sr)

# Aggregate chroma features between beat events
# We'll use the median value of each feature between beat frames
beat_chroma = librosa.util.sync(chromagram,
                                beat_frames,
                                aggregate=np.median)

# Finally, stack all beat-synchronous features together
beat_features = np.vstack([beat_chroma, beat_mfcc_delta])

This example builds on tools we’ve already covered in the quickstart example, so here we’ll focus just on the new parts.

The first difference is the use of the effects module for time-series harmonic-percussive separation:

y_harmonic, y_percussive = librosa.effects.hpss(y)

The result of this line is that the time series y has been separated into two time series, containing the harmonic (tonal) and percussive (transient) portions of the signal. Each of y_harmonic and y_percussive have the same shape and duration as y.

The motivation for this kind of operation is two-fold: first, percussive elements tend to be stronger indicators of rhythmic content, and can help provide more stable beat tracking results; second, percussive elements can pollute tonal feature representations (such as chroma) by contributing energy across all frequency bands, so we’d be better off without them.

Next, we introduce the feature module and extract the Mel-frequency cepstral coefficients from the raw signal y:

mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_mfcc=13)

The output of this function is the matrix mfcc, which is an numpy.ndarray of size (n_mfcc, T) (where T denotes the track duration in frames). Note that we use the same hop_length here as in the beat tracker, so the detected beat_frames values correspond to columns of mfcc.

The first type of feature manipulation we introduce is delta, which computes (smoothed) first-order differences among columns of its input:

mfcc_delta = librosa.feature.delta(mfcc)

The resulting matrix mfcc_delta has the same shape as the input mfcc.

The second type of feature manipulation is sync, which aggregates columns of its input between sample indices (e.g., beat frames):

beat_mfcc_delta = librosa.util.sync(np.vstack([mfcc, mfcc_delta]),
                                    beat_frames)

Here, we’ve vertically stacked the mfcc and mfcc_delta matrices together. The result of this operation is a matrix beat_mfcc_delta with the same number of rows as its input, but the number of columns depends on beat_frames. Each column beat_mfcc_delta[:, k] will be the average of input columns between beat_frames[k] and beat_frames[k+1]. (beat_frames will be expanded to span the full range [0, T] so that all data is accounted for.)

Next, we compute a chromagram using just the harmonic component:

chromagram = librosa.feature.chroma_cqt(y=y_harmonic,
                                        sr=sr)

After this line, chromagram will be a numpy.ndarray of size (12, T), and each row corresponds to a pitch class (e.g., C, C#, etc.). Each column of chromagram is normalized by its peak value, though this behavior can be overridden by setting the norm parameter.

Once we have the chromagram and list of beat frames, we again synchronize the chroma between beat events:

beat_chroma = librosa.util.sync(chromagram,
                                beat_frames,
                                aggregate=np.median)

This time, we’ve replaced the default aggregate operation (average, as used above for MFCCs) with the median. In general, any statistical summarization function can be supplied here, including np.max(), np.min(), np.std(), etc.

Finally, the all features are vertically stacked again:

beat_features = np.vstack([beat_chroma, beat_mfcc_delta])

resulting in a feature matrix beat_features of dimension (12 + 13 + 13, # beat intervals).

More examples

More example scripts are provided in the advanced examples section.