2025:Audio Key Detection - Revision history

Chestnut: /* Dataset */

2025-05-30T17:17:29Z

‎Dataset

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‎Dataset

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‎Dataset

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‎Train Dataset

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‎Data (need update)

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‎README File

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‎Data (need update)

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‎Data (need update)

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‎Description

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 === Test Datasets ===
-GiantSteps Key Dataset
+==== GiantSteps Key Dataset ====
 The GiantSteps Key Dataset comprises 604 two-minute excerpts of electronic dance music tracks, primarily sourced from Beatport. Each excerpt is annotated with one of 24 musical keys—12 major and 12 minor—providing a standardized benchmark for evaluating key classification performance.
-Isophonics Dataset
+==== Isophonics Dataset ====
 The Isophonics Dataset includes 225 songs from artists such as The Beatles, Queen, and Zweieck. Each track is annotated with key, chord, beat, and structural segmentation information, offering a comprehensive resource for evaluating key detection algorithms across a diverse range of popular music.

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 For the audio key detection task, we do not impose restrictions on the training data used by machine learning-based systems. However, all submissions must clearly state the specific training dataset(s) used in their extended abstract.
-=== Test Dataset ===
+=== Test Datasets ===
-The test dataset for this year is the GiantSteps Key Dataset, which consists of 604 two-minute excerpts of electronic dance music tracks, originally sourced from Beatport. Each excerpt is annotated with one of 24 musical keys—12 major and 12 minor—serving as a standardized benchmark for evaluating key classification performance.
+GiantSteps Key Dataset
 == Evaluation Procedures ==

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 === Test Dataset ===
 The test dataset for this year is the GiantSteps Key Dataset, which consists of 604 two-minute excerpts of electronic dance music tracks, originally sourced from Beatport. Each excerpt is annotated with one of 24 musical keys—12 major and 12 minor—serving as a standardized benchmark for evaluating key classification performance.
 == Evaluation Procedures ==

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 Audio Key Detection aims to identify the musical key (e.g., C major, A minor) of an audio recording. This involves determining both the tonic (root pitch) and the mode (major or minor) from the audio signal.
-== Train Dataset ==
+== Dataset ==
 For the audio key detection task, we do not impose restrictions on the training data used by machine learning-based systems. However, all submissions must clearly state the specific training dataset(s) used in their extended abstract.

@@ Line 3: / Line 3: @@
 Audio Key Detection aims to identify the musical key (e.g., C major, A minor) of an audio recording. This involves determining both the tonic (root pitch) and the mode (major or minor) from the audio signal.
-== Data (need update) ==
+== Train Dataset ==
-* need to state: all datasets for training is okay
+For the audio key detection task, we do not impose restrictions on the training data used by machine learning-based systems. However, all submissions must clearly state the specific training dataset(s) used in their extended abstract.
-=== Collections ===
+=== Test Dataset ===
-The collection used for this year's evaluation is the same as the one used in 2005. It consists of 1252 classical music audio pieces rendered from MIDI using the timidity MIDI synthesizer. The ground-truth key is drawn from the title of the piece. The entire piece is not used, but rather the first 30 seconds. This is done because usually the beginnings of pieces are in the labeled key before they possibly deviate due to key modulation.
+The test dataset for this year is the GiantSteps Key Dataset, which consists of 604 two-minute excerpts of electronic dance music tracks, originally sourced from Beatport. Each excerpt is annotated with one of 24 musical keys—12 major and 12 minor—serving as a standardized benchmark for evaluating key classification performance.
 === Audio Formats ===

← Older revision		Revision as of 16:21, 30 May 2025
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	foobar -param1 .1 -i %input -o %output		foobar -param1 .1 -i %input -o %output
−
−	~~For a submission using MATLAB, the README file could look like:~~
−
−	~~matlab -r "foobar(.1,'%input','%output');quit;"~~

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 ==Description==
-Determination of the key is a prerequisite for any analysis of tonal music. As a result, extensive work has been done in the area of automatic key detection. The goal of this task is the identification of the key from music in audio format.
+Audio Key Detection aims to identify the musical key (e.g., C major, A minor) of an audio recording. This involves determining both the tonic (root pitch) and the mode (major or minor) from the audio signal.
 == Data (need update) ==

← Older revision		Revision as of 16:52, 20 May 2025
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	matlab -r "foobar(.1,'%input','%output');quit;"		matlab -r "foobar(.1,'%input','%output');quit;"
−
−	~~== 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 will be imposed.~~
−
−	~~A hard limit of 6 hours will be imposed on analysis times.~~