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Description
This is the original dataset containing 51582 music tracks (22 music genres) and 173 element-feature vector [1-6,9]. A collection of more than 50000 music excerpts described with a set of descriptors obtained through the analysis of 30-second mp3 recordings was gathered in a database called SYNAT. The SYNAT database was realized by the Gdansk University of Technology (GUT) [1,2]. For the recordings included in the database, the analysis band is limited to 8 kHz due to the music excerpts format, this means that the frequency band used for the parameterization is in the range from 63 to 8000 Hz. The prepared feature vector is used to describe parametrically each signal frame. The original database stores 173‑feature vectors, which in majority are the MPEG-7 standard parameters [7], however we used also the so-called 'dedicated' features, described in several publications.
The original 173-element vector has additionally been supplemented with 20 Mel-
Frequency Cepstral Coefficients (MFCC), 20 MFCC variances and 24 time-related
‘dedicated’ parameters. The vector includes parameters associated with the MPEG-7
standard, mel-cepstral (MFCC) parameters and is enlarged by the so-called dedicated
parameters which refer to temporal characteristic of the analyzed music excerpt, their
names are included in Table 1. The list of parameters and their definitions were shown in
the earlier studies [10][11], however, it is worth noting that the proposed FV was used in the ISMIS 2011 contest in which there were over 120 participants [4]. The best contest result returned almost 88% of accuracy [4], and later in the authors’ own study gained even
better effectiveness [8].
Table 1 The list of parameters within the SYNAT music database [10].
|
No. |
Parameter |
|
1 |
Temporal Centroid |
|
2 |
Spectral Centroid |
|
3 |
Spectral Centroid variance |
|
4-32 |
Audio Spectrum Envelope for particular bands |
|
33 |
ASE average for all bands |
|
34-62 |
ASE variance values for particular bands |
|
63 |
averaged ASE variance |
|
64 |
average Audio Spectrum Centroid |
|
65 |
variance of Audio Spectrum Centroid |
|
66 |
average Audio Spectrum Spread |
|
67 |
variance Audio Spectrum Spread |
|
68-87 |
Spectral Flatness Measure for particular bands |
|
88 |
SFM average value |
|
89-108 |
Spectral Flatness Measure variance for particular bands |
|
109 |
averaged SFM variance |
|
110-129 |
Mel-Frequency Cepstral Coefficients for particular bands |
|
130-149 |
MFCC variance for particular bands |
|
150 |
number of samples exceeding RMS |
|
151 |
number of samples exceeding 2×RMS |
|
152 |
number of samples exceeding 3×RMS |
|
153 |
mean value of samples exceeding RMS, averaged for 10 frames |
|
154 |
variance value of samples exceeding RMS, averaged for 10 frames |
|
155 |
mean value of samples exceeding 2×RMS, averaged for 10 frames |
|
156 |
variance value of samples exceeding 2×RMS, averaged for 10 frames |
|
157 |
mean value of samples exceeding 3×RMS, averaged for 10 frames |
|
158 |
variance value of samples exceeding 3×RMS, averaged for 10 frames |
|
159 |
peak to RMS ratio |
|
160 |
mean value of the peak to RMS ratio calculated in 10 subframes |
|
161 |
variance of the peak to RMS ratio calculated in 10 subframes |
|
162 |
Zero Crossing Rate |
|
163 |
RMS Threshold Crossing Rate |
|
164 |
2×RMS Threshold Crossing Rate |
|
165 |
3×RMS Threshold Crossing Rate |
|
166 |
Zero Crossing Rate averaged for 10 frames |
|
167 |
Zero Crossing Rate variance for 10 frames |
|
168 |
RMS Threshold Crossing Rate averaged for 10 frames |
|
169 |
RMS Threshold Crossing Rate variance for 10 frames |
|
170 |
2×RMS Threshold Crossing Rate averaged for 10 frames |
|
171 |
2×RMS Threshold Crossing Rate variance for 10 frames |
|
172 |
3×RMS Threshold Crossing Rate averaged for 10 frames |
|
173 |
3×RMS Threshold Crossing Rate variance for 10 frames |
[1] Kostek B., Music Information Retrieval in Music Repositories, Rough Sets and Intelligent Systems (A. Skowron, Z. Suraj, eds.), 464-489, Springer Verlag, Berlin, Heildelberg 2013. https://doi.org/10.1007/978-3-642-30344-9_17
[2] Kostek B., Hoffmann P., Kaczmarek A., Spaleniak P., Creating a Reliable Music Discovery and Recommendation System, Springer Verlag, 107-130, XIII, 2013. DOI: 10.1007/978-3-319-04714-0_7
[3] Hoffmann P., Kostek B., Music Data Processing and Mining in Large Databases for Active Media, The 2014 16 International Conference on Active Media Technology, Warsaw, 85-85, Springer 2014. https://doi.org/10.1007/978-3-319-09912-5_8
[4] Kostek B., Kupryjanow A., Zwan P, Jiang W., Ras Z., Wojnarski M., Swietlicka J., Report of the ISMIS 2011 Contest: Music Information Retrieval, Foundations of Intelligent Systems, ISMIS 2011, Springer Verlag, 715–724, Berlin, Heidelberg 2011. https://doi.org/10.1007/978-3-642-21916-0_75
[5] Rosner A., Schuller B., Kostek B., Classification of Music Genres Based on Music Separation into Harmonic and Drum Components. Archives of Acoustics, 629-638, 2014, DOI: 10.2478/aoa-2014-0068.
[6] Kostek B., Kaczmarek A., Music Recommendation Based on Multidimensional Description and Similarity Measures, Fundamenta Informaticae, 127(1-4), 325-340, 2013. DOI: 10.3233/FI-2013-912.
[7] MPEG 7 standard, http://mpeg.chiariglione.org/standards/mpeg-7
[8] Hoffmann P., Kostek B., Kaczmarek A., Spaleniak P., Music Recommendation System, Journal of Telecommunication and Information Technology, 59-69, Warsaw 2013.
[9] Hoffmann P., Kostek B., Bass Enhancement Settings in Portable Devices Based on Music Genre Recognition, Journal of the Audio Engineering Society, Vol. 63, No. 12, 980-989, December 2015, DOI: http://dx.doi.org/10.17743/jaes.2015.0087
[10] Rosner A., Kostek B. Automatic music genre classification based on musical instrument track separation. J Intell Inf Syst 50, 363–384 (2018). https://doi.org/10.1007/s10844-017-0464-5
[11] Plewa M., Kostek B., Music Mood Visualization Using Self-Organizing Maps; Archives of Acoustics, No. 4, vol. 40, pp. 513 - 525, 2015, DOI: 10.1515/aoa-2015-0051.
Dataset file
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.Example script for calculation:
https://github.com/antespi/s3md5
File details
- License:
-
open in new tab
CC BYAttribution
Details
- Year of publication:
- 2021
- Verification date:
- 2021-06-22
- Dataset language:
- English
- Fields of science:
-
- information and communication technology (Engineering and Technology)
- DOI:
- DOI ID 10.34808/zcyv-3s55 open in new tab
- Verified by:
- Gdańsk University of Technology
Keywords
References
- dataset SYNAT Music Genre Parameters PCA 19
- dataset SYNAT_PCA_48
- dataset SYNAT_PCA_11
- publication Music Information Retrieval in Music Repositories
- publication Classification of Music Genres Based on Music Separation into Harmonic and Drum Components . Klasyfikacja gatunków muzycznych wykorzystująca separację instrumentów muzycznych
- publication Music Data Processing and Mining in Large Databases for Active Media
- publication Report of the ISMIS 2011 Contest : Music Information Retrieval
- publication Music Recommendation Based on Multidimensional Description and Similarity Measures . Rekomendacja muzyki na podstawie wielowymiarowego wektora cech i miar podobieństwa
- publication Music Recommendation System
- publication Bass Enhancement Settings in Portable Devices Based on Music Genre Recognition
- publication Automatic music genre classification based on musical instrument track separation / Automatyczna klasyfikacja gatunku muzycznego wykorzystująca algorytm separacji dźwięku instrumentó muzycznych
- publication Music Mood Visualization Using Self-Organizing Maps
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