2018:Music and/or Speech Detection - Revision history

Blai Melendez-Catalan: /* Content */

2018-09-19T09:25:11Z

‎Content

Blai Melendez-Catalan: /* Music Relative Loudness Estimation */

2018-08-28T08:24:39Z

‎Music Relative Loudness Estimation

Blai Melendez-Catalan: /* Music Relative Loudness Estimation */

2018-08-28T08:24:18Z

‎Music Relative Loudness Estimation

Blai Melendez-Catalan: /* Command line calling format */

2018-08-07T08:48:57Z

‎Command line calling format

Blai Melendez-Catalan: /* Music Relative Loudness Estimation */

2018-08-03T11:14:02Z

‎Music Relative Loudness Estimation

Blai Melendez-Catalan: /* Speech Detection */

2018-08-03T11:12:34Z

‎Speech Detection

Blai Melendez-Catalan: /* Music Detection */

2018-08-03T11:12:25Z

‎Music Detection

Blai Melendez-Catalan: /* Music Relative Loudness Estimation */

2018-08-03T11:12:06Z

‎Music Relative Loudness Estimation

Blai Melendez-Catalan: /* Foreground-Music/Background-Music/No-Music Segmentation */

2018-08-03T11:11:38Z

‎Foreground-Music/Background-Music/No-Music Segmentation

Blai Melendez-Catalan: /* Event-level evaluation: */

2018-08-03T11:01:33Z

‎Event-level evaluation:

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 ====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 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 48,000 Hz with 16 bits per sample. Around 50% of the audio contains music, from which 70% is background music (35% of the total audio content). This background music can have a very low volume and be covered by any kind of non-musical sounds such as speech, audience noises, sound effects, everyday-life sounds, sounds of the city, etc. The annotation style includes a limit to how short an event can be of 2 seconds for music and background music and 1 second for speech.
-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.
+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====

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 ===Music Relative Loudness Estimation===
-The music relative loudness estimation sub-task consists in detecting segments of one of the following two classes: foreground music and background music. 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.
+The music relative loudness estimation sub-task consists in finding segments of one of the following two classes: foreground music and background music. 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

@@ Line 29: / Line 29: @@
 ===Music Relative Loudness Estimation===
-The music relative loudness estimation sub-task consists in segregating the whole audio in segments of one of the following three classes: foreground music, background music and no music. 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.
+The music relative loudness estimation sub-task consists in detecting segments of one of the following two classes: foreground music and background music. 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
+classes: fg-music, bg-music
 ==Datasets==

← Older revision		Revision as of 08:48, 7 August 2018
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	Music and Speech Detection: ''doMusicAndSpeechDetection path/to/file.wav path/to/output/file.muspd ''		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~~ ''	+	Music Relative Loudness Estimation: ''doMusicRelLoudEstimation path/to/file.wav path/to/output/file.mrle ''

@@ Line 29: / Line 29: @@
 ===Music Relative Loudness Estimation===
-The music relative loudness estimation sub-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.
+The music relative loudness estimation sub-task consists in segregating the whole audio in segments of one of the following three classes: foreground music, background music and no music. 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

@@ Line 17: / Line 17: @@
 ===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.
+The speech detection sub-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

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 ===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.
+The music detection sub-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

@@ Line 27: / Line 27: @@
 classes: music, speech
-===Foreground-Music/Background-Music/No-Music Segmentation===
+===Music Relative Loudness Estimation===
 The foreground-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.

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 * N is the number of ref events.
-Different tolerance time-windows will be used: +/- 500 ms, +/- 200 ms, +/- 100 ms.
+Different tolerance time-windows will be used: +/- 1000 ms, +/- 500 ms, +/- 200 ms, +/- 100 ms.
 ===Other evaluated features===