2018:Music and/or Speech Detection

Description

The need for music and/or speech detection is evident in many audio processing tasks which relate to real-life materials such as archives of field recordings, broadcasts and any other contexts which are likely to involve speech and music, concurrent or alternating. Segregating the signal into speech and music segments is an obvious first step before applying speech-specific or music-specific algorithms.

Indeed, music and/or speech detection has received considerable attention from the research community (for a partial list, see references below) but many of the published algorithms are dataset-specific and are not directly comparable due to non-standardised evaluation.

This Mirex task is aimed at filling that gap and consists of three sub-tasks: Music Detection, Speech Detection and Music and Speech Detection, with submissions welcomed to one or more of them.

Tasks

Music Detection

The music detection task consists in finding segments of music in a signal. This task applies to complete recordings from archives. No assumptions are made about the number of segments present in each archive or about their duration.

classes: music

Speech Detection

The speech detection task consists in finding segments of speech in a signal. This task applies to complete recordings from archives. No assumptions are made about the number of segments present in each archive or about their duration.

classes: speech

Music and Speech Detection

The music and speech detection sub-task is a combination of the previous two sub-tasks, i.e., the submitted algorithms will have to find segments of music and speech. No assumptions are made about the number of segments present in each archive or about their duration. Moreover, they might overlap in time.

classes: music, speech

Music/Background-music/No-music Segmentation

The music/background-music/no-music segmentation task consists in segregating the whole audio in segments of one of these three classes. This task applies to complete recordings from archives. No assumptions are made about the number of segments present in each archive or about their duration.

classes: fg-music, bg-music, no-music

Datasets

Available Training Datasets

These resources may be a good starting point for participants.

GTZAN Speech and Music Dataset http://opihi.cs.uvic.ca/sound/music_speech.tar.gz

Scheirer & Slaney Music Speech Corpus http://www.ee.columbia.edu/~dpwe/sounds/musp/scheislan.html

MUSAN Corpus http://www.openslr.org/17/

Muspeak Speech and Music Detection Dataset http://mirg.city.ac.uk/datasets/muspeak/muspeak-mirex2015-detection-examples.zip

Music detection dataset: www.seyerlehner.info/download/music_detection_dataset_dafx_07.zip (Ask the author for the password)

Evaluation Dataset

Content

Evaluation dataset 1: it consists of 27 hours of audio from 8 different TV program types from France, Germany, Spain and the United Kingdom. It includes 1647 1-minute files sampled at 22,050 or 44,100 Hz with 16 bits per sample. We split this dataset into a training dataset (30 %) and a testing dataset (70 %). In order to avoid too similar training and testing datasets, audio files coming from the same country and program type are only present in one of them.

Evaluation dataset 2: it consists of 10 hours of audio corresponding to French TV and radio programs, provided by INA (French National Institute of Audiovisual). This include archives collected from 1950 up to now. The files were sampled at 16,000 Hz with 16 bits per sample. The whole dataset will be used for evaluation. It is aimed at being used with pretrained models only.

Annotation

Evaluation dataset 1: it was annotated by a single annotator using BAT. A percentage of the annotations has been manually reviewed. The classes included in the ground truth are: foreground music, background music and no music. We defined foreground music as music that is louder than all the other existing simultaneous sounds.

Evaluation dataset 2: has been manually annotated using Transcriber. Corpus annotation has been realized in the framework of the European Funded project MeMAD. It contains two possibly overlapping classes: speech and music.

Evaluation

In the literature we find two ways of measuring the performance of an algorithm depending on the way we compare the ground truth with an algorithm's estimation: the frame-level evaluation and the event-level evaluation. We will report the statistics for each of these evaluations by file and for the whole dataset. We will do that for each algorithm and dataset.

Frame-level evaluation:

In the frame-level evaluation, we compare the estimation (est) produced by the algorithms with the ground truth (gt) in frames of 10 ms. We first compute the intermediate statistics for each class C, which include:

• True Positives (TPc): gt frame’s class = C & est frame’s class = C
• False Positives (FPc): gt frame’s class != C & est frame’s class = C
• True Negatives (TNc): gt frame’s class != C & est frame’s class != C
• False Negatives (FNc): gt frame’s class = C & est frame’s class != C

Then we report class-wise Precision, Recall and F-measure.

• Precision (Pc) = TPc / (TPc + FPc)
• Recall (Rc) = TPc / (TPc + FNc)
• F-measure = 2 * Pc * Rc / (Pc + Rc)

As well as the overall Accuracy:

• Accuracy = (TP + TN) / (TP + TN + FP + FN)

Where:

• TP = sum(TPc), for every class c
• FP = sum(FPc), for every class c
• TN = sum(TNc), for every class c
• FN = sum(FNc), for every class c

Event-level evaluation:

In the event-level evaluation, we compare the estimation (est) produced by the algorithms with the ground truth (gt) in terms of onsets and offsets. We first compute the intermediate statistics for the onsets and offsets of each class C, which include:

• True Positives (TP): an est onset (offset) of class = C appears in a tolerance time-window around a gt onset (offset) of class = C.
• False Positives (FP): an est onset (offset) of class = C appears outside of the tolerance time-window of any gt onset (offset) of class = C.
• False Negatives (FN): no est onset (offset) of class = C appears inside of the tolerance time-window of a gt onset (offset) of class = C.

Then we report class-wise Precision, Recall and F-measure.

• Precision (Pc) = TPc / (TPc + FPc)
• Recall (Rc) = TPc / (TPc + FNc)
• F-measure = 2 * Pc * Rc / (Pc + Rc)

As well as the overall Accuracy:

• Accuracy = (TP + TN) / (TP + TN + FP + FN)

Where:

• TP = sum(TPc), for every class c
• FP = sum(FPc), for every class c
• TN = sum(TNc), for every class c
• FN = sum(FNc), for every class c

Different tolerance time-window will be used: +/- 500 ms, +/- 200 ms, +/- 100 ms.

Other evaluated features

The execution time of each algorithm will also be reported.

Submission Format

Command line calling format

Submissions have to conform to the specified format below:

Music Detection: doMusicDetection path/to/file.wav path/to/output/file.mud

Speech Detection: doSpeechDetection path/to/file.wav path/to/output/file.spd

Music and Speech Detection: doMusicAndSpeechDetection path/to/file.wav path/to/output/file.muspd

Fg.-Music/Bg.-Music/No-music segmentation: doFgBgNoMusicSegmentation path/to/file.wav path/to/output/file.fbns

where:

• path/to/file.wav: Path to the input audio file.
• path/to/output/file.*: The output file.

Programs can use their working directory if they need to keep temporary cache files or internal debugging info. Stdout and stderr will be logged.

I/O format

For each detected segment, the file should include a row containing the onset (seconds), duration (seconds) and the class separated by a tab and ordered by onset time:

onset1    	duration    	class
onset2    	duration    	class
...  ... 	...

(note that events in the case of music and speech detection can overlap)

Packaging submissions

All submissions should be statically linked to all libraries (the presence of dynamically linked libraries cannot be guaranteed) and include a README file including the following the information:

• Command line calling format for all executables and an example formatted set of commands
• Number of threads/cores used or whether this should be specified on the command line
• Expected memory footprint
• Expected runtime
• Any required environments (and versions), e.g. python, java, bash, matlab.

Potential Participants

name/email

Jan Schlüter, jan.schlueter ... ofai.at David Doukhan, ddoukhan … ina.fr Blai Meléndez-Catalán, bmelendez … bmat.com

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Time and hardware limits

Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions are specified. A hard limit of 72 hours will be imposed on runs. Submissions that exceed this runtime may not receive a result.

Submission closing date

The submission deadline for these tasks is the 11th of August.

Task specific mailing list

All discussions on this task will take place on the MIREX "EvalFest" list. If you have a question or comment, simply include the task name in the subject heading.