SYNAT Music Genre Parameters PCA 19 - Open Research Data - Bridge of Knowledge

Your account

login

Search

SYNAT Music Genre Parameters PCA 19

Description

The dataset contains feature vector after  Principal Component Analysis (PCA) performing, so there are 11 music genres and 19-element vector derived from music excerpts. Originally, a feature vector containing 173 elements was conceived in earlier research studies carried out by the team of authors [1-6]. A collection of 52532 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 173-feature dataset is also available in Most Wiedzy.

173-element vector generates a very large amount of information describing a given track. As a consequence, this leads to an extensive amount of data undergoing classification. Therefore, Principal Component Analysis (PCA) was applied to reduce the data redundancy as it transforms a number of possibly correlated variables into a smaller number of variables called principal components. The new components are linear combination of parameters that carry most information about the test set, thus they are no longer refer to descriptors contained in the original feature vector.  The PCA method can shorten the feature vector of 173 elements to 19 components, which significantly reduces the computation time. Furthermore, the use of the described analysis can increase classification efficiency, as shown in an earlier paper [9].

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 study [2],[5], 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 original music database [2][10][11].

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] Kostek B., Hoffmann P., Music Data Processing and Mining in Large Databases for Active Media, The 2014 16International Conference on Active Media Technology, Warsaw, 2014.

[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

Synat__PCA_19_genres_11.csv
5.8 MB, S3 ETag 9141b73f99d5bb0da29a8a7f357f8146-1, downloads: 92
The file hash is calculated from the formula
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:
Creative Commons: by 4.0 open in new tab
CC BY
Attribution

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 10.34808/3rxz-fy33 open in new tab
Verified by:
Gdańsk University of Technology

Keywords

References

Cite as

Authors

seen 248 times