JAMS

A JSON Annotated Music Specification for Reproducible MIR Research.

JAMS provides:

• A formal JSON schema for generic annotations
• The ability to store multiple annotations per file
• Schema definitions for a wide range of annotation types (beats, chords, segments, tags, etc.)
• Error detection and validation
• A translation layer to interface with mir_eval for evaluating annotations

For the most recent information, please refer to JAMS on github.

Getting started

Creating a JAMS data structure from scratch

First, create the top-level JAMS container:

>>> import jams
>>> jam = jams.JAMS()

A track in JAMS must have a duration (in seconds). For this example, we’ll make up a fake number, but in reality, you would compute the track duration from the source audio.

>>> jam.file_metadata.duration = 8.0

Now we can create a beat annotation:

>>> ann = jams.Annotation(namespace='beat', time=0, duration=jam.file_metadata.duration)
>>> ann.append(time=0.33, duration=0.0, confidence=1, value=1)

Then, we’ll update the annotation’s metadata by directly setting its fields:

>>> ann.annotation_metadata = jams.AnnotationMetadata(data_source='Well paid students')
>>> ann.annotation_metadata.curator = jams.Curator(name='Rincewind',
...                                                email='rincewind@unseen.edu')

Add our new annotation to the jam:

>>> jam.annotations.append(ann)

We can update the annotation at any time, and add a new observation:

>>> ann.append(time=0.66, duration=0.0, confidence=1, value=1)

Once you’ve added all your data, you can serialize the annotation to a string:

>>> jam.dumps(indent=2)
{
  "sandbox": {},
  "annotations": [
    {
      "data": [
        {
          "duration": 0.0,
          "confidence": 1.0,
          "value": 1.0,
          "time": 0.33
        },
        {
          "duration": 0.0,
          "confidence": 1.0,
          "value": 1.0,
          "time": 0.66
        }
      ],
      "annotation_metadata": {
        "annotation_tools": "",
        "curator": {
          "name": "Rincewind",
          "email": "rincewind@unseen.edu"
        },
        "annotator": {},
        "version": "",
        "corpus": "",
        "annotation_rules": "",
        "validation": "",
        "data_source": "Well paid students"
      },
      "namespace": "beat",
      "sandbox": {}
    }
  ],
  "file_metadata": {
    "jams_version": "0.2.0",
    "title": "",
    "identifiers": {},
    "release": "",
    "duration": 8.0,
    "artist": ""
  }
}

Or save to a file using the built-in save function:

>>> jam.save("these_are_still_my.jams")

Reading a JAMS file

Assuming you already have a JAMS file on-disk, say at ‘these_are_also_my.jams’, you can easily read it back into memory:

>>> another_jam = jams.load('these_are_also_my.jams')

JAMS Structure

This section describes the anatomy of JAMS objects.

JAMS

A JAMS object consists of three basic properties:

• file_metadata, which describes the audio file to which these annotations are attached;
• annotations, a list of Annotation objects (described below); and
• sandbox, an unrestricted place to store any additional data.

FileMetadata

The file_metadata field contains the following properties:

• identifiers: an unstructured sandbox-type object for storing identifier mappings, e.g., MusicBrainz ID;
• artist, title, release : meta-data strings for the track in question;
• duration : non-negative number describing the length (in seconds) of the track; and
• jams_version : string describing the JAMS version for this file.

Annotation

Each annotation object contains the following properties:

• namespace : a string describing the type of this annotation;

• data : a list of observations, each containing:

  • time : non-negative number denoting the time of the observation (in seconds)
  • duration : non-negative number denoting the duration of the observation (in seconds)
  • value : actual annotation (e.g., chord, segment label)
  • confidence : certainty of the annotation

• annotation_metadata : see Annotation_Metadata; and
• sandbox : additional unstructured storage space for this annotation.
• time : optional non-negative number indicating the beginning point at which this annotation is valid
• duration : optional non-negative number indicating the duration of the valid portion of this annotation.

The permissible contents of the value and confidence fields are defined by the namespace.

Note

The time and duration fields of annotation are considered optional. If left blank, the annotation should be assumed to be valid for the entirety of the track.

Annotation_Metadata

The meta-data associated with each annotation describes the process by which the annotation was generated. The annotation_metadata property has the following fields:

• corpus: a string describing a corpus to which this annotation belongs;
• version : string or number, the version of this annotation;
• curator : a structured object containing contact information (name and email) for the curator of this data;
• annotator : a sandbox object to describe the individual annotator — which can be a person or a program — that generated this annotation;
• annotation_tools, annotation_rules, validation: strings to describe the process by which annotations were collected and pre-processed; and
• data_source : string describing the type of annotator, e.g., “program”, “expert human”, “crowdsource”.

Task namespaces

In JAMS v0.2.0, the concept of task namespaces was introduced. Broadly speaking, a namespace defines the syntax (and some semantics) of a particular type of annotation.

For example, the chord namespace requires that all observed value fields are valid strings within a pre-defined grammar. Similarly, the tempo namespace requires that value fields be non-negative numbers, and the confidence fields lie within the range [0, 1].

JAMS ships with 26 pre-defined namespaces, covering a variety of common music informatics tasks. This collection should not be assumed to be complete, however, and more namespaces may be added in subsequent versions. Please refer to Namespace definitions for a comprehensive description of the existing namespaces.

Namespace specification format

In this section, we’ll demonstrate how to define a task namespace, using tempo as our running example. Namespaces are defined by JSON objects that contain partial JSON schema specifications for the value and confidence fields of the Annotation, as well as additional meta-data to describe the namespace and encoding.

tempo.json is reproduced here:

{"tempo":
    {
        "value": {
            "type": "number",
            "minimum": 0
        },
        "confidence": {
            "type": "number",
            "minimum": 0,
            "maximum": 1.0
        },
        "dense": false,
        "description": "Tempo measurements, in beats per minute (BPM)"
    }
}

The key “tempo” at line 1 is the string with which this namespace will be identified in JAMS objects by the annotation’s namespace field. This string must be a unique identifier.

Lines 3–6 specify the valid contents of the value field for tempo annotations. In this case, values must be numeric and non-negative. Any valid JSON schema definition can be substituted here, allowing for structured observation objects. (See pattern_jku for an example of this.)

Similarly, lines 7–11 specify valid contents of the confidence field. Most namespaces do not enforce specific constraints on confidence, so this block is optional. In the case of tempo, confidence must be a numeric value in the range [0, 1].

Line 12 dense is a boolean which specifies whether the annotation should be densely encoded during serialization or not. There is functionally no difference between dense and sparse encoding, but dense coding is more space-efficient for high-frequency observations such as melody contours.

Finally, line 13 contains a brief description of the namespace and corresponding task.

Local namespaces

The JAMS namespace management architecture is modular and extensible, so it is relatively straightforward to create a new namespace schema and add it to JAMS at run-time:

>>> jams.schema.add_namespace('/path/to/my/new/namespace.json')

Beginning with JAMS 0.2.1, a custom schema directory can be provided by setting the JAMS_SCHEMA_DIR environment variable prior to importing jams. This allows local customizations to be added automatically at run-time without having to manually add each schema file individually.

Example usage

Storing annotations

This section demonstrates a complete use-case of JAMS for storing estimated annotations. The example uses librosa to estimate global tempo and beat timings.

example_beat.py

The following script loads the librosa example audio clip, estimates the track duration, tempo, and beat timings, and constructs a JAMS object to store the estimations.

#!/usr/bin/env python

import librosa
import jams


def beat_track(infile, outfile):

    # Load the audio file
    y, sr = librosa.load(infile)

    # Compute the track duration
    track_duration = librosa.get_duration(y=y, sr=sr)

    # Extract tempo and beat estimates
    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)

    # Convert beat frames to time
    beat_times = librosa.frames_to_time(beat_frames, sr=sr)

    # Construct a new JAMS object and annotation records
    jam = jams.JAMS()

    # Store the track duration
    jam.file_metadata.duration = track_duration

    beat_a = jams.Annotation(namespace='beat')
    beat_a.annotation_metadata = jams.AnnotationMetadata(data_source='librosa beat tracker')

    # Add beat timings to the annotation record.
    # The beat namespace does not require value or confidence fields,
    # so we can leave those blank.
    for t in beat_times:
        beat_a.append(time=t, duration=0.0)

    # Store the new annotation in the jam
    jam.annotations.append(beat_a)

    # Add tempo estimation to the annotation.
    tempo_a = jams.Annotation(namespace='tempo', time=0, duration=track_duration)
    tempo_a.annotation_metadata = jams.AnnotationMetadata(data_source='librosa tempo estimator')

    # The tempo estimate is global, so it should start at time=0 and cover the full
    # track duration.
    # If we had a likelihood score on the estimation, it could be stored in
    # `confidence`.  Since we have no competing estimates, we'll set it to 1.0.
    tempo_a.append(time=0.0,
                   duration=track_duration,
                   value=tempo,
                   confidence=1.0)

    # Store the new annotation in the jam
    jam.annotations.append(tempo_a)

    # Save to disk
    jam.save(outfile)


if __name__ == '__main__':

    infile = librosa.util.example_audio_file()
    beat_track(infile, 'output.jams')

example_beat_output.jams

The above script generates the following JAMS object.

{
  "sandbox": {},
  "file_metadata": {
    "duration": 61.45886621315193,
    "title": "",
    "release": "",
    "identifiers": {},
    "artist": "",
    "jams_version": "0.2.3"
  },
  "annotations": [
    {
      "sandbox": {},
      "duration": null,
      "data": [
        {
          "value": null,
          "confidence": null,
          "time": 0.11609977324263039,
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          "duration": 0.0
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          "duration": 0.0
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          "duration": 0.0
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        {
          "value": null,
          "confidence": null,
          "time": 49.458503401360545,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 49.92290249433107,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 50.387301587301586,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 50.85170068027211,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 51.2928798185941,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 51.757278911564626,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 52.22167800453515,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 52.68607709750567,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 53.15047619047619,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 53.614875283446715,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 54.05605442176871,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 54.52045351473923,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 54.98485260770975,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 55.44925170068027,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 55.913650793650795,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 56.37804988662131,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 56.842448979591836,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 57.30684807256236,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 57.77124716553288,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 58.2356462585034,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 58.6768253968254,
          "duration": 0.0
        },
        {
          "value": null,
          "confidence": null,
          "time": 59.14122448979592,
          "duration": 0.0
        }
      ],
      "namespace": "beat",
      "time": 0,
      "annotation_metadata": {
        "corpus": "",
        "validation": "",
        "annotation_tools": "",
        "version": "",
        "curator": {
          "name": "",
          "email": ""
        },
        "annotation_rules": "",
        "annotator": {},
        "data_source": "librosa beat tracker"
      }
    },
    {
      "sandbox": {},
      "duration": 61.45886621315193,
      "data": [
        {
          "value": 129.19921875,
          "confidence": 1.0,
          "time": 0.0,
          "duration": 61.45886621315193
        }
      ],
      "namespace": "tempo",
      "time": 0,
      "annotation_metadata": {
        "corpus": "",
        "validation": "",
        "annotation_tools": "",
        "version": "",
        "curator": {
          "name": "",
          "email": ""
        },
        "annotation_rules": "",
        "annotator": {},
        "data_source": "librosa tempo estimator"
      }
    }
  ]
}

Evaluating annotations

The following script illustrates how to evaluate one JAMS annotation object against another using the built-in eval submodule to wrap mir_eval.

Given two jams files, say, reference.jams and estimate.jams, the script first loads them as objects (j_ref and j_est, respectively). It then uses the JAMS.search method to locate all annotations of namespace "beat". If no matching annotations are found, an empty list is returned.

In this example, we are assuming that each JAMS file contains only a single annotation of interest, so the first result is taken by indexing the results at 0. (In general, you may want to use annotation_metadata to select a specific annotation from the JAMS object, if multiple are present.)

Finally, the two annotations are compared by calling jams.eval.beat, which returns an ordered dictionary of evaluation metrics for the annotations in question.

example_eval.py

#!/usr/bin/env python

import sys
import jams

from pprint import pprint

def compare_beats(f_ref, f_est):

    # f_ref contains the reference annotations
    j_ref = jams.load(f_ref)

    # f_est contains the estimated annotations
    j_est = jams.load(f_est)

    # Get the first reference beats
    beat_ref = j_ref.search(namespace='beat')[0]
    beat_est = j_est.search(namespace='beat')[0]

    # Get the scores
    return jams.eval.beat(beat_ref, beat_est)


if __name__ == '__main__':

    f_ref, f_est = sys.argv[1:]
    scores = compare_beats(f_ref, f_est)

    # Print them out
    pprint(dict(scores))

Data conversion

JAMS provides some basic functionality to help convert from flat file formats (e.g., CSV or LAB).

example_chord_import.py

#!/usr/bin/env python

import jams
import sys


def import_chord_jams(infile, outfile):

    # import_lab returns a new jams object,
    # and a handle to the newly created annotation
    chords = jams.util.import_lab('chord', infile)

    # Infer the track duration from the end of the last annotation
    duration = max([obs.time + obs.duration for obs in chords])

    chords.time = 0
    chords.duration = duration

    # Create a jams object
    jam = jams.JAMS()
    jam.file_metadata.duration = duration
    jam.annotations.append(chords)

    # save to disk
    jam.save(outfile)


if __name__ == '__main__':

    infile, outfile = sys.argv[1:]
    import_chord_jams(infile, outfile)

chord_output.jams

Calling the above script on 01_-_I_Saw_Her_Standing_There.lab from IsoPhonics should produce the following JAMS object:

{
  "annotations": [
    {
      "duration": 175.804082,
      "data": [
        {
          "duration": 2.6122669999999997,
          "value": "N",
          "confidence": 1.0,
          "time": 0.0
        },
        {
          "duration": 8.846803000000001,
          "value": "E",
          "confidence": 1.0,
          "time": 2.6122669999999997
        },
        {
          "duration": 1.4628569999999996,
          "value": "A",
          "confidence": 1.0,
          "time": 11.45907
        },
        {
          "duration": 4.521546999999998,
          "value": "E",
          "confidence": 1.0,
          "time": 12.921927
        },
        {
          "duration": 2.966888000000001,
          "value": "B",
          "confidence": 1.0,
          "time": 17.443474
        },
        {
          "duration": 1.497686999999999,
          "value": "E",
          "confidence": 1.0,
          "time": 20.410362
        },
        {
          "duration": 1.4628580000000007,
          "value": "E:7/3",
          "confidence": 1.0,
          "time": 21.908049
        },
        {
          "duration": 1.4860770000000016,
          "value": "A",
          "confidence": 1.0,
          "time": 23.370907
        },
        {
          "duration": 1.486076999999998,
          "value": "A:min/b3",
          "confidence": 1.0,
          "time": 24.856984
        },
        {
          "duration": 1.497686999999999,
          "value": "E",
          "confidence": 1.0,
          "time": 26.343061
        },
        {
          "duration": 1.5092970000000037,
          "value": "B",
          "confidence": 1.0,
          "time": 27.840747999999998
        },
        {
          "duration": 5.955917999999997,
          "value": "E",
          "confidence": 1.0,
          "time": 29.350045
        },
        {
          "duration": 1.497686999999999,
          "value": "A",
          "confidence": 1.0,
          "time": 35.305963
        },
        {
          "duration": 4.459452000000006,
          "value": "E",
          "confidence": 1.0,
          "time": 36.80365
        },
        {
          "duration": 2.982543999999997,
          "value": "B",
          "confidence": 1.0,
          "time": 41.263102
        },
        {
          "duration": 1.474466999999997,
          "value": "E",
          "confidence": 1.0,
          "time": 44.245646
        },
        {
          "duration": 1.4860770000000016,
          "value": "E:7/3",
          "confidence": 1.0,
          "time": 45.720113
        },
        {
          "duration": 1.4860770000000016,
          "value": "A",
          "confidence": 1.0,
          "time": 47.20619
        },
        {
          "duration": 1.4628569999999996,
          "value": "A:min/b3",
          "confidence": 1.0,
          "time": 48.692267
        },
        {
          "duration": 1.497686999999999,
          "value": "E",
          "confidence": 1.0,
          "time": 50.155124
        },
        {
          "duration": 1.4860770000000016,
          "value": "B",
          "confidence": 1.0,
          "time": 51.652811
        },
        {
          "duration": 2.9721550000000008,
          "value": "E",
          "confidence": 1.0,
          "time": 53.138888
        },
        {
          "duration": 9.020951999999987,
          "value": "A",
          "confidence": 1.0,
          "time": 56.111043
        },
        {
          "duration": 3.0185940000000073,
          "value": "B",
          "confidence": 1.0,
          "time": 65.13199499999999
        },
        {
          "duration": 3.0418140000000022,
          "value": "A",
          "confidence": 1.0,
          "time": 68.150589
        },
        {
          "duration": 3.0069840000000028,
          "value": "E",
          "confidence": 1.0,
          "time": 71.192403
        },
        {
          "duration": 1.497686999999999,
          "value": "A",
          "confidence": 1.0,
          "time": 74.199387
        },
        {
          "duration": 4.539501000000001,
          "value": "E",
          "confidence": 1.0,
          "time": 75.697074
        },
        {
          "duration": 2.9721550000000008,
          "value": "B",
          "confidence": 1.0,
          "time": 80.236575
        },
        {
          "duration": 3.012962999999999,
          "value": "E",
          "confidence": 1.0,
          "time": 83.20873
        },
        {
          "duration": 1.5149279999999976,
          "value": "A",
          "confidence": 1.0,
          "time": 86.221693
        },
        {
          "duration": 1.5209070000000082,
          "value": "A:min/b3",
          "confidence": 1.0,
          "time": 87.736621
        },
        {
          "duration": 1.4628569999999854,
          "value": "E",
          "confidence": 1.0,
          "time": 89.25752800000001
        },
        {
          "duration": 1.4370680000000107,
          "value": "B",
          "confidence": 1.0,
          "time": 90.720385
        },
        {
          "duration": 11.949235999999999,
          "value": "E",
          "confidence": 1.0,
          "time": 92.157453
        },
        {
          "duration": 3.0185940000000073,
          "value": "B",
          "confidence": 1.0,
          "time": 104.106689
        },
        {
          "duration": 3.0534239999999926,
          "value": "E",
          "confidence": 1.0,
          "time": 107.12528300000001
        },
        {
          "duration": 2.9453800000000143,
          "value": "A",
          "confidence": 1.0,
          "time": 110.178707
        },
        {
          "duration": 1.4896309999999744,
          "value": "E",
          "confidence": 1.0,
          "time": 113.12408700000002
        },
        {
          "duration": 1.4860770000000088,
          "value": "B",
          "confidence": 1.0,
          "time": 114.61371799999999
        },
        {
          "duration": 2.845166000000006,
          "value": "E",
          "confidence": 1.0,
          "time": 116.099795
        },
        {
          "duration": 9.101501000000013,
          "value": "A",
          "confidence": 1.0,
          "time": 118.944961
        },
        {
          "duration": 3.0069839999999886,
          "value": "B",
          "confidence": 1.0,
          "time": 128.04646200000002
        },
        {
          "duration": 2.983764000000008,
          "value": "A",
          "confidence": 1.0,
          "time": 131.053446
        },
        {
          "duration": 3.006984999999986,
          "value": "E",
          "confidence": 1.0,
          "time": 134.03721000000002
        },
        {
          "duration": 1.4313290000000052,
          "value": "A",
          "confidence": 1.0,
          "time": 137.044195
        },
        {
          "duration": 4.582638999999972,
          "value": "E",
          "confidence": 1.0,
          "time": 138.475524
        },
        {
          "duration": 2.9837640000000363,
          "value": "B",
          "confidence": 1.0,
          "time": 143.05816299999998
        },
        {
          "duration": 1.5092969999999752,
          "value": "E",
          "confidence": 1.0,
          "time": 146.04192700000002
        },
        {
          "duration": 1.5092970000000037,
          "value": "E:7/3",
          "confidence": 1.0,
          "time": 147.551224
        },
        {
          "duration": 1.451246999999995,
          "value": "A",
          "confidence": 1.0,
          "time": 149.060521
        },
        {
          "duration": 1.5092970000000037,
          "value": "A:min/b3",
          "confidence": 1.0,
          "time": 150.511768
        },
        {
          "duration": 1.509297000000032,
          "value": "E",
          "confidence": 1.0,
          "time": 152.021065
        },
        {
          "duration": 1.5325169999999844,
          "value": "B",
          "confidence": 1.0,
          "time": 153.53036200000003
        },
        {
          "duration": 4.469842,
          "value": "E",
          "confidence": 1.0,
          "time": 155.062879
        },
        {
          "duration": 1.5325169999999844,
          "value": "B",
          "confidence": 1.0,
          "time": 159.532721
        },
        {
          "duration": 4.516280999999992,
          "value": "E",
          "confidence": 1.0,
          "time": 161.065238
        },
        {
          "duration": 1.5325170000000128,
          "value": "B",
          "confidence": 1.0,
          "time": 165.581519
        },
        {
          "duration": 1.5325170000000128,
          "value": "A",
          "confidence": 1.0,
          "time": 167.114036
        },
        {
          "duration": 1.0908560000000023,
          "value": "E",
          "confidence": 1.0,
          "time": 168.646553
        },
        {
          "duration": 1.9497639999999876,
          "value": "E:9",
          "confidence": 1.0,
          "time": 169.737409
        },
        {
          "duration": 4.116908999999993,
          "value": "N",
          "confidence": 1.0,
          "time": 171.687173
        }
      ],
      "namespace": "chord",
      "time": 0,
      "annotation_metadata": {
        "version": "",
        "annotation_tools": "",
        "annotator": {},
        "curator": {
          "email": "",
          "name": ""
        },
        "data_source": "",
        "corpus": "",
        "annotation_rules": "",
        "validation": ""
      },
      "sandbox": {}
    }
  ],
  "file_metadata": {
    "duration": 175.804082,
    "jams_version": "0.2.3",
    "artist": "",
    "identifiers": {},
    "release": "",
    "title": ""
  },
  "sandbox": {}
}

More examples

In general, converting a dataset to JAMS format will require a bit more work to ensure that value fields conform to the specified namespace schema, but the import script above should serve as a simple starting point.

For further reference, a separate repository jams-data has been created to house conversion scripts for publicly available datasets. Note that development of converters is a work in progress, so proceed with caution!

API reference

API Reference

Core functionality

This library provides an interface for reading JAMS into Python, or creating them programatically.

Function reference

load(path_or_file[, validate, strict, fmt])

Load a JAMS Annotation from a file.

Object reference

JAMS([annotations, file_metadata, sandbox])	Top-level Jams Object
FileMetadata([title, artist, release, …])	Metadata for a given audio file.
AnnotationArray([annotations])	This list subclass provides serialization and search/filtering for annotation collections.
AnnotationMetadata([curator, version, …])	Data structure for metadata corresponding to a specific annotation.
Curator([name, email])	Container object for curator metadata.
Annotation(namespace[, data, …])	Annotation base class.
Observation(time, duration, value, confidence)	Core observation type: (time, duration, value, confidence).
Sandbox(unconstrained)	Functionally identical to JObjects, but the class hierarchy might be confusing if all objects inherit from Sandboxes.
JObject(**kwargs)	Dict-like object for JSON Serialization.
Observation(time, duration, value, confidence)	Core observation type: (time, duration, value, confidence).

Namespace management

add_namespace(filename)	Add a namespace definition to our working set.
namespace(ns_key)	Construct a validation schema for a given namespace.
namespace_array(ns_key)	Construct a validation schema for arrays of a given namespace.
is_dense(ns_key)	Determine whether a namespace has dense formatting.
values(ns_key)	Return the allowed values for an enumerated namespace.
get_dtypes(ns_key)	Get the dtypes associated with the value and confidence fields for a given namespace.
list_namespaces()	Print out a listing of available namespaces

Display

display(annotation[, meta])	Visualize a jams annotation through mir_eval
display_multi(annotations[, fig_kw, meta])	Display multiple annotations with shared axes

Sonification

sonify(annotation[, sr, duration])

Sonify a jams annotation through mir_eval

Evaluation

beat(ref, est, \*\*kwargs)	Beat tracking evaluation
chord(ref, est, \*\*kwargs)	Chord evaluation
melody(ref, est, \*\*kwargs)	Melody extraction evaluation
onset(ref, est, \*\*kwargs)	Onset evaluation
segment(ref, est, \*\*kwargs)	Segment evaluation
tempo(ref, est, \*\*kwargs)	Tempo evaluation
pattern(ref, est, \*\*kwargs)	Pattern detection evaluation
hierarchy(ref, est, \*\*kwargs)	Multi-level segmentation evaluation
transcription(ref, est, \*\*kwargs)	Note transcription evaluation

Namespace conversion

convert(annotation, target_namespace)

Convert a given annotation to the target namespace.

Utility functions

import_lab(namespace, filename[, infer_duration])	Load a .lab file as an Annotation object.
expand_filepaths(base_dir, rel_paths)	Expand a list of relative paths to a give base directory.
smkdirs(dpath[, mode])	Safely make a full directory path if it doesn’t exist.
filebase(filepath)	Return the extension-less basename of a file path.
find_with_extension(in_dir, ext[, depth, sort])	Naive depth-search into a directory for files with a given extension.

Namespace definitions

Beat

beat

Beat event markers with optional metrical position.

time	duration	value	confidence
[sec]	[sec]	[number] or [null]	–

Each observation corresponds to a single beat event.

The value field can be a number (positive or negative, integer or floating point), indicating the metrical position within the bar of the observed beat.

If no metrical position is provided for the annotation, the value field will be null.

Example

time	duration	value	confidence
0.500	0.000	1	null
1.000	0.000	2	null
1.500	0.000	3	null
2.000	0.000	4	null
2.500	0.000	1	null

Note

duration is typically zero for beat events, but this is not enforced.

confidence is an unconstrained field for beat annotations, and may contain arbitrary data.

beat_position

Beat events with time signature information.

time	duration	value	confidence
[sec]	[sec]	position measure num_beats beat_units	–

Each observation corresponds to a single beat event.

The value field is a structure containing the following fields:

• position : the position of the beat within the measure. Can be any number greater than or equal to 1.
• measure : the index of the measure containing this beat. Can be any non-negative integer.
• num_beats : the number of beats per measure : can be any strictly positive integer.
• beat_units : the note value for beats in this measure. Must be one of: 1, 2, 4, 8, 16, 32, 64, 128, 256.

All fields are required for each observation.

Example

time	duration	value	confidence
0.500	0.000	position: 1 measure: 0 num_beats: 4 beat_units: 4	null
1.000	0.000	position: 2 measure: 0 num_beats: 4 beat_units: 4	null
1.500	0.000	position: 3 measure: 0 num_beats: 4 beat_units: 4	null
2.000	0.000	position: 4 measure: 0 num_beats: 4 beat_units: 4	null
2.500	0.000	position: 1 measure: 1 num_beats: 4 beat_units: 4	null

Note

duration is typically zero for beat events, but this is not enforced.

confidence is an unconstrained field for beat annotations, and may contain arbitrary data.

position should lie in the range [1, beat_units], but the upper bound is not enforced at the schema level.

Chord

chord

Chord annotations described by an extended version of the grammar defined by Harte, et al. [1]

time	duration	value	confidence
[sec]	[sec]	string	–

[1]	(1, 2, 3) Harte, Christopher, Mark B. Sandler, Samer A. Abdallah, and Emilia Gómez. “Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations.” In ISMIR, vol. 5, pp. 66-71. 2005.

This namespace is similar to chord_harte, with the following modifications:

• Sharps and flats may not be mixed in a note symbol. For instance, A#b# is legal in chord_harte but not in chord. A### is legal in both.

• The following quality values have been added:

  • sus2, 1, 5
  • aug7
  • 11, maj11, min11
  • 13, maj13, min13

Example

time	duration	value	confidence
0.000	1.000	N	null
0.000	1.000	Bb:5	null
0.000	1.000	E:(*5)	null
0.000	1.000	E#:min9/9	null
0.000	1.000	G##:maj6	null
0.000	1.000	D:13/6	null
0.000	1.000	A:sus2	null

Note

confidence is an unconstrained field, and may contain arbitrary data.

chord_harte

Chord annotations described according to the grammar defined by Harte, et al. [1]

time	duration	value	confidence
[sec]	[sec]	string	–

Each observed value is a text representation of a chord annotation.

• N specifies a no chord observation
• Notes are annotated in the usual way: A-G followed by optional sharps (#) and flats (b)

• Chord qualities are denoted by abbreviated strings:

  • maj, min, dim, aug
  • maj7, min7, 7, dim7, hdim7, minmaj7
  • maj6, min6
  • 9, maj9, min9
  • sus4

• Inversions are specified by a slash (/) followed by the interval number, e.g., G/3.
• Extensions are denoted in parentheses, e.g., G(b11,13). Suppressed notes are indicated with an asterisk, e.g., G(*3)

A complete description of the chord grammar is provided in [1], table 1.

Example

time	duration	value	confidence
0.000	1.000	N	null
0.000	1.000	Bb	null
0.000	1.000	E:(*5)	null
0.000	1.000	E#:min9/9	null
0.000	1.000	G#b:maj6	null
0.000	1.000	D/6	null
0.000	1.000	A:sus4	null

Note

confidence is an unconstrained field, and may contain arbitrary data.

chord_roman

Chord annotations in roman numeral format, as described by [2].

time	duration	value	confidence
[sec]	[sec]	tonic chord	–

The value field is a structure containing the following fields:

• tonic : (string) the tonic note of the chord, e.g., A or Gb.

• chord : (string) the scale degree of the chord in roman numerals (1–7), along with inversions, extensions, and qualities.

  • Scale degrees are encoded with optional leading sharps and flats, e.g., V, bV or #VII. Upper-case numerals indicate major, lower-case numeral indicate minor.

  • Qualities are encoded as one of the following symbols:

    • o : diminished (triad)
    • + : augmented (triad)
    • s : suspension
    • d : dominant (seventh)
    • h : half-diminished (seventh)
    • x : fully-diminished (seventh)

  • Inversions are encoded by arabic numerals, e.g., V6 for a first-inversion triad, V64 for second inversion.

  • Applied chords are encoded by a / followed by a roman numeral encoding of the scale degree, e.g., V7/IV.

[2]	(1, 2) http://theory.esm.rochester.edu/rock_corpus/harmonic_analyses.html

Example

time	duration	value	confidence
0.000	0.500	tonic: C chord: I6	–
0.500	0.500	tonic: C chord: bIV	–
1.000	0.500	tonic: C chord: Vh7	–

Note

The grammar defined in [2] has been constrained to support only the quality symbols listed above.

confidence is an unconstrained field, and may contain arbitrary data.

Key

key_mode

Key and optional mode (major/minor or Greek modes)

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is a string matching one of the three following patterns:

• N : no key
• Ab, A, A#, Bb, ... G# : tonic note, upper case
• tonic:MODE where tonic is as described above, and MODE is one of: major, minor, ionian, dorian, phrygian, lydian, mixolydian, aeolian, locrian.

Example

time	duration	value	confidence
0.000	30.0	C:minor	null
30.0	5.00	N	null
35.0	15.0	C#:dorian	null
50.0	10.0	Eb	null
60.0	10.0	A:lydian	null

Note

confidence is an unconstrained field, and may contain arbitrary data.

Lyrics

lyrics

Time-aligned lyrical annotations.

time	duration	value	confidence
[sec]	[sec]	string	–

The required value field can contain arbitrary text data, e.g., lyrics.

Example

time	duration	value	confidence
0.500	4.000	“Row row row your boat”	null
4.500	2.000	“gently down the stream”	null
7.000	1.000	“merrily”	null
8.000	1.000	“merrily”	null
9.000	1.000	“merrily”	null
10.00	1.000	“merrily”	null

Note

confidence is an unconstrained field, and may contain arbitrary data.

lyrics_bow

Time-aligned bag-of-words or bag-of-ngrams.

time	duration	value	confidence
[sec]	[sec]	array	–

The required value field is an array, where each element is an array of [term, count]. The term here may be either a string (for simple bag-of-words) or an array of strings (for bag-of-ngrams).

Example

time	duration	value	confidence
0.000	30.00	[‘row’, 3] [ [‘row’, ‘row’], 2] [‘your’, 1] [‘boat’, 1]	null

Note

confidence is an unconstrained field, and may contain arbitrary data.

Mood

mood_thayer

Time-varying emotion measurements as ordered pairs of (valence, arousal)

time	duration	value	confidence
[sec]	[sec]	(valence, arousal)	–

The value field is an ordered pair of numbers measuring the valence and arousal positions in the Thayer mood model [3].

[3]	Thayer, Robert E. The biopsychology of mood and arousal. Oxford University Press, 1989.

Example

time	duration	value	confidence
0.500	0.250	(-0.5, 1.0)	null
0.750	0.250	(-0.3, 0.6)	null
1.000	0.750	(-0.1, 0.1)	null
1.750	0.500	(0.3, -0.5)	null

Note

confidence is an unconstrained field, and may contain arbitrary data.

Onset

onset

Note onset event markers.

time	duration	value	confidence
[sec]	[sec]	–	–

This namespace can be used to encode timing of arbitrary instantaneous events. Most commonly, this is applied to note onsets.

Example

time	duration	value	confidence
0.500	0.000	null	null
1.000	0.000	null	null
1.500	0.000	null	null
2.000	0.000	null	null

Note

duration is typically zero for instantaneous events, but this is not enforced.

value and confidence fields are unconstrained, and may contain arbitrary data.

Pattern

pattern_jku

Each note of the pattern contains (pattern_id, midi_pitch, occurrence_id, morph_pitch, staff), following the format described in [4].

time	duration	value	confidence
[sec]	[sec]	pattern_id midi_pitch occurrence_id morph_pitch staff	–

[4]	Collins T., Discovery of Repeated Themes & Sections, Music Information Retrieval Evalaluation eXchange (MIReX), 2013 (Accessed on July 7th 2015). Available here.

Each value field contains a dictionary with the following keys:

• pattern_id: The integer that identifies the current pattern,

  starting from 1.

• midi_pitch: The float representing the midi pitch.

• occurrence_id: The integer that identifies the current occurrence,

  starting from 1.

• morph_pitch: The float representing the morphological pitch.

• staff: The integer representing the staff where the current note of the

  patter is found, starting from 0.

Example

time	duration	value	confidence
62.86	0.09	pattern_id: 1 midi_pitch: 50 occurrence_id: 1 morph_pitch: 54 staff: 1	null
62.86	0.36	pattern_id: 1 midi_pitch: 77 occurrence_id: 1 morph_pitch: 70 staff: 0	null
36.34	0.09	pattern_id: 1 midi_pitch: 71 occurrence_id: 2 morph_pitch: 66 staff: 0	null
36.43	0.36	pattern_id: 1 midi_pitch: 69 occurrence_id: 2 morph_pitch: 65 staff: 0	null

Pitch

pitch_contour

Pitch contours in the format (index, frequency, voicing).

time	duration	value	confidence
[sec]	[–]	index frequency voiced	–

Each value field is a structure containing a contour index (an integer indicating which contour the observation belongs to), a frequency value in Hz, and a boolean indicating if the values is voiced. The confidence field is unconstrained.

Example

time	duration	value	confidence
0.0000	0.0000	index: 0 frequency: 442.1 voiced: True	null
0.0058	0.0000	index: 0 frequency: 457.8 voiced: False	null
2.5490	0.0000	index: 1 frequency: 89.4 voiced: True	null
2.5548	0.0000	index: 1 frequency: 90.0 voiced: True	null

note_hz

Note events with (non-negative) frequencies measured in Hz.

time	duration	value	confidence
[sec]	[sec]	number	–

Each value field gives the frequency of the note in Hz.

Example

time	duration	value	confidence
12.34	0.287	189.9	null
2.896	3.000	74.0	null
10.12	0.5.	440.0	null

note_midi

Note events with pitches measured in (fractional) MIDI note numbers.

time	duration	value	confidence
[sec]	[sec]	number	–

Each value field gives the pitch of the note in MIDI note numbers.

Example

time	duration	value	confidence
12.34	0.287	52.0	null
2.896	3.000	20.7	null
10.12	0.5.	42.0	null

pitch_class

Pitch measurements in (tonic, pitch class) format.

time	duration	value	confidence
[sec]	[sec]	tonic pitch class	–

Each value field is a structure containing a tonic (note string, e.g., "A#" or "D") and a pitch class pitch as an integer scale degree. The confidence field is unconstrained.

Example

time	duration	value	confidence
0.000	30.0	tonic: C pitch: 0	null
0.000	30.0	tonic: C pitch: 4	null
0.000	30.0	tonic: C pitch: 7	null
30.00	35.0	tonic: G pitch: 0	null

pitch_hz

Warning

Deprecated, use pitch_contour.

Pitch measurements in Hertz (Hz). Pitch (a subjective sensation) is represented as fundamental frequency (a physical quantity), a.k.a. “f0”.

time	duration	value	confidence
[sec]	–	number	–

The time field represents the instantaneous time in which the pitch f0 was estimated. By convention, this (usually) represents the center time of the analysis frame. Note that this is different from pitch_midi and pitch_class, where time represents the onset time. As a consequence, the duration field is undefined and should be ignored. The value field is a number representing the f0 in Hz. By convention, values that are equal to or less than zero are used to represent silence (no pitch). Some algorithms (e.g. melody extraction algorithms that adhere to the MIREX convention) use negative f0 values to represent the algorithm’s pitch estimate for frames where it thinks there is no active pitch (e.g. no melody), to allow the independent evaluation of pitch activation detection (a.k.a. “voicing detection”) and pitch frequency estimation. The confidence field is unconstrained.

Example

time	duration	value	confidence
0.000	0.000	300.00	null
0.010	0.000	305.00	null
0.020	0.000	310.00	null
0.030	0.000	0.00	null
0.040	0.000	-280.00	null
0.050	0.000	-290.00	null

pitch_midi

Warning

Deprecated, use note_midi or pitch_contour.

Pitch measurements in (fractional) MIDI note number notation.

time	duration	value	confidence
[sec]	[sec]	number	–

The value field is a number representing the pitch in MIDI notation. Numbers can be negative (for notes below C-1) or fractional.

Example

time	duration	value	confidence
0.000	30.000	24	null
0.000	30.000	43.02	null
15.00	45.000	26	null

Segment

segment_open

Structural segmentation with an open vocabulary of segment labels.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field contains string descriptors for each segment, e.g., “verse” or “bridge”.

Example

time	duration	value	confidence
0.000	20.000	intro	null
20.00	30.000	verse	null
30.00	50.000	refrain	null
50.00	70.000	verse (alternate)	null

segment_salami_function

Segment annotations with functional labels from the SALAMI guidelines.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field must be one of the allowable strings in the SALAMI function vocabulary.

Example

time	duration	value	confidence
0.000	20.000	applause	null
20.00	30.000	count-in	null
30.00	50.000	introduction	null
50.00	70.000	verse	null

segment_salami_upper

Segment annotations with SALAMI’s upper-case (large) label format.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field must be a string of the following format:

• “silence” or “Silence”
• One or more upper-case letters, followed by zero or more apostrophes

Example

time	duration	value	confidence
0.000	20.000	silence	null
20.00	30.000	A	null
30.00	50.000	B	null
50.00	70.000	A’	null

segment_salami_lower

Segment annotations with SALAMI’s lower-case (small) label format.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field must be a string of the following format:

• “silence” or “Silence”
• One or more lower-case letters, followed by zero or more apostrophes

Example

time	duration	value	confidence
0.000	20.000	silence	null
20.00	30.000	a	null
30.00	50.000	b	null
50.00	70.000	a’	null

segment_tut

Segment annotations using the TUT vocabulary.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is a string describing the function of the segment.

Example

time	duration	value	confidence
0.000	20.000	Intro	null
20.00	30.000	Verse	null
30.00	50.000	bridge	null
50.00	70.000	RefrainA	null

multi_segment

Multi-level structural segmentations.

time	duration	value	confidence
[sec]	[sec]	label : string level : int >= 0	–

In a multi-level segmentation, the track is partitioned many times — possibly recursively — which results in a collection of segmentations of varying degrees of specificity. In the multi_segment namespace, all of the resulting segments are collected together, and the level field is used to encode the segment’s corresponding partition.

Level values must be non-negative, and ordered by increasing specificity. For example, level==0 may correspond to a single segment spanning the entire track, and each subsequent level value corresponds to a more refined segmentation.

Example

time	duration	value	confidence
0.000	60.000	label : A level : 0	null
0.000	30.000	label : B level : 1	null
30.00	60.000	label : C level : 1	null
0.000	15.000	label : a level : 2	null
15.00	30.000	label : b level : 2	null
30.00	45.000	label : a level : 2	null
45.00	60.000	label : c level : 2	null

Tag

tag_cal10k

Tags from the CAL10K vocabulary.

time	duration	value	confidence
[sec]	[sec]	string	–

The value is constrained to a set of 1053 terms, spanning mood, instrumentation, style, and genre.

time	duration	value	confidence
0.000	30.000	“bop influences”	null
0.000	30.000	“bright beats”	null
0.000	30.000	“hip hop roots”	null

tag_cal500

Tags from the CAL500 vocabulary.

time	duration	value	confidence
[sec]	[sec]	string	–

The value is constrained to a set of 174 terms, spanning mood, instrumentation, and genre.

time	duration	value	confidence
0.000	30.000	“Genre-Best-Rock”	null
0.000	30.000	“Vocals-Monotone”	null
0.000	30.000	“Usage-At_work”	null

tag_gtzan

Genre classes from the GTZAN dataset.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to one of ten strings:

• blues
• classical
• country
• disco
• hip-hop
• jazz
• metal
• pop
• reggae
• rock

By convention, only one tag is applied per track in this namespace. This is not enforced by the schema.

Example

time	duration	value	confidence
0.000	30.000	“reggae”	null

tag_msd_tagtraum_cd1

Genre classes from the msd tagtraum cd1 dataset.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to one of 13 strings:

• reggae
• pop/rock
• rnb
• jazz
• vocal
• new age
• latin
• rap
• country
• international
• blues
• electronic
• folk

By convention, one or two tags per track are possible in this namespace. The sum of the confidence values should equal 1.0. This is not enforced by the schema.

Example

time	duration	value	confidence
0.000	0.000	“reggae”	1.0

tag_msd_tagtraum_cd2

Genre classes from the msd tagtraum cd2 dataset.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to one of 15 strings:

• reggae
• latin
• metal
• rnb
• jazz
• punk
• pop
• new age
• country
• rap
• rock
• world
• blues
• electronic
• folk

By convention, one or two tags per track are possible in this namespace. The sum of the confidence values should equal 1.0. This is not enforced by the schema.

Example

time	duration	value	confidence
0.000	0.000	“reggae”	0.6666667
0.000	0.000	“rock”	0.3333333

tag_medleydb_instruments

MedleyDB instrument source annotations.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to the set of instruments defined in MedleyDB instrument taxonomy.

Example

time	duration	value	confidence
0.000	20.000	“darbuka”	null
0.000	20.000	“flute section”	null
0.000	20.000	“oud”	null

tag_open

Open vocabulary tags. This namespace is appropriate for unconstrained tag data, such as tags from Last.FM or Magnatagatune.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is unconstrained, and can contain any string value.

Example

time	duration	value	confidence
0.000	20.000	“rocking”	null
0.000	20.000	“rockin’”	null
0.000	20.000	“”	null
0.000	20.000	“favez^^^”	null

tag_audioset

Tags from the full AudioSet (v1) ontology.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to the vocabulary of the AudioSet ontology.

Example

time	duration	value	confidence
0.000	20.000	“Air brake”	null
5.000	25.000	“Yodeling”	null
9.000	35.000	“Steam whistle”	null

tag_audioset_genre

Tags from the musical genre subset of the AudioSet (v1) ontology.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to the 66 musical genres of the AudioSet-genre ontology.

Example

time	duration	value	confidence
0.000	20.000	“Oldschool jungle”	null

tag_audioset_instrument

Tags from the musical instrument subset of the AudioSet (v1) ontology.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to the 91 musical instruments of the AudioSet-instruments ontology.

Example

time	duration	value	confidence
0.000	20.000	“Ukulele”	null
5.000	25.000	“Piano”	null
9.000	35.000	“Tuning fork”	null

tag_fma_genre

Tags from the Free Music Archive (FMA) 16-class genre taxonomy.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to the 16 genres of the FMA data-set.

Example

time	duration	value	confidence
0.000	20.000	“Blues”	null
5.000	25.000	“Instrumental”	null
9.000	35.000	“Soul-RnB”	null

tag_fma_subgenre

Tags from the Free Music Archive (FMA) 163-class genre and sub-genre taxonomy.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to the 163 genres and sub-genres of the FMA data-set.

Example

time	duration	value	confidence
0.000	20.000	“Pop”	null
5.000	25.000	“Power-Pop”	null
9.000	35.000	“Nerdcore”	null

tag_urbansound

Genre classes from the UrbanSound dataset.

time	duration	value	confidence
[sec]	[sec]	string	–

The value field is constrained to one of ten strings:

• air_conditioner
• car_horn
• children_playing
• dog_bark
• drilling
• engine_idling
• gun_shot
• jackhammer
• siren
• street_music

Example

time	duration	value	confidence
0.000	30.000	“street_music”	null

Tempo

tempo

Tempo measurements in beats per minute (BPM).

time	duration	value	confidence
[sec]	[sec]	number	number

The value field is a non-negative number (floating point), indicated the tempo measurement. The confidence field is a number in the range [0, 1], following the format used by MIREX [5].

[5]	http://www.music-ir.org/mirex/wiki/2014:Audio_Tempo_Estimation

Example

time	duration	value	confidence
0.00	60.00	180.0	0.8
0.00	60.00	90.0	0.2

Note

MIREX requires that tempo measurements come in pairs, and that the confidence values sum to 1. This is not enforced at the schema level.

Miscellaneous

Vector

Numerical vector data. This is useful for generic regression problems where the output is a vector of numbers.

time	duration	value	confidence
[sec]	[sec]	[array of numbers]	–

Each observation value must be an array of at least one number. Different observations may have different length arrays, so it is up to the user to verify that arrays have the desired length.

Blob

Arbitrary data blobs.

time	duration	value	confidence
[sec]	[sec]	–	–

This namespace can be used to encode arbitrary data. The value and confidence fields have no schema constraints, and may contain any structured (but serializable) data. This can be useful for storing complex output data that does not fit any particular task schema, such as regression targets or geolocation data.

It is strongly advised that the AnnotationMetadata for blobs be as explicit as possible.

Scaper

Structured representation for soundscapes synthesized by the Scaper package.

time	duration	value	confidence
[sec]	[sec]	label source_file source_time event_time event_duration snr time_stretch pitch_shift role	–

Each value field contains a dictionary with the following keys:

• label: a string indicating the label of the sound source
• source_file: a full path to the original sound source (on disk)
• source_time: a non-negative number indicating the time offset within source_file of the sound
• event_time: the start time of the event in the synthesized soundscape
• event_duration: a strictly positive number indicating the duration of the event
• snr: the signal-to-noise ratio (in LUFS) of the sound compared to the background
• time_stetch: (optional) a strictly positive number indicating the amount of time-stretch applied to the source
• pitch_shift: (optional) the amount of pitch-shift applied to the source
• role: one of background or foreground

Contribute

• Issue Tracker
• Source Code

Changelog

Changes

v0.3.4

• Added support for jsonschema version 3.0 (PR #202)
• Added converter script jams_to_mirex_pattern.py (PR #194)

v0.3.3

• Pinned jsonschema dependency to version 2.6 (PR #200)

v0.3.2

• Added schemata for tag_urbansound and scaper (PR #191)
• Fixed a timing bug in Annotation.slice (PR #189)
• Added display mapping for tag_open namespaces (PR #188)
• Updated sortedcontainers dependency to avoid deprecations (PR #187)

v0.3.1

• Improved documentation (PR #176)
• Added Annotation.to_samples (PR #173)
• Added schemata for FMA genre tags (PR #172)
• Accelerated validation (PR #170)
• Added schemata for AudioSet tags (PR #168)
• Added jams.list_namespaces() (PR #166)

v0.3.0

• Removed the JamsFrame class and replaced the underlying observation storage data structure (PR #149).
• import_lab now returns only an Annotation and does not construct a JAMS object (PR #154)
• Accelerated pitch contour sonification (PR #155)
• Migrated all tests from nosetest to py.test (PR #157)
• Improved repr() and added HTML rendering for JAMS objects in notebooks (PR #158)
• Fixed a JSON serialization bug with numpy datatypes (PR #160)

v0.2.3

• Deprecated the JamsFrame class (PR #153):
  • Moved JamsFrame.to_interval_values() to Annotation.to_interval_values()
  • Any code that uses pandas.DataFrame methods on Annotation.data will cease to work starting in 0.3.0.
• Forward compatibility with 0.3.0 (PR #153):
  • Added the jams.Observation type
  • Added iteration support to Annotation objects
• added type safety check in regexp search (PR #146).
• added support for pandas=0.20 (PR #150).

v0.2.2

• added __contains__ method to JObject (PR #139).
• Implemented JAMS.trim() method (PR #136).
• Updates to the SALAMI tag namespaces (PR #134).
• added infer_duration flag to import_lab (PR #125).
• namespace conversion validates input (PR #123).
• Refactored the pitch namespaces (PR #121).
• Fancy indexing for annotation arrays (PR #120).
• jams.schema.values function to access enumerated types (PR #119).
• jams.display submodule (PR #115).
• support for mir_eval >= 0.3 (PR #106).
• Automatic conversion between namespaces (PR #105).
• Fixed a type error in jams_to_lab (PR #94).
• jams.sonify module for sonification (PR #91).

v0.2.1

New features

• eval support for hierarchical segmentation via the multi_segment namespace (PR #79).
• Local namespace management (PR #75).
• Python 3.5 support (PR #73).
• jams.search() now allows matching objects by equality (PR #71).
• multi_segment namespace for multi-level structural segmentations. (PR #69).
• vector namespace for numerical vector data (PR #64).
• blob namespace for unstructured, time-keyed observation data (PR #63).
• tag_msd_tagtraum_cd1 and tag_msd_tagtraum_cd2 namespaces for genre tags (PR #83).

Schema changes

• Annotation objects now have time and duration fields which encode the interval over which the annotation is valid. (PR #67).

Bug fixes

• Appending data to Annotation or JamsFrame objects now fails if time or duration are ill-specified. (PR #87).

• Index