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Table 3 MFCC + spectral contrast classification results

From: Singer identification model using data augmentation and enhanced feature conversion with hybrid feature vector and machine learning

Article

Dataset

Features

Classifiers

Rate(%)

Liu and Huang (2002) [46]

Ten different singers, 30 different music for each singer (Chinese)

FMCV, PMCV

KNN

80.0

Tsai and Wang (2006) [47]

Twenty-three different singers, 10 different music for each singer

MFCC

GMM

87.8

Dharini and Revathy (2014) [26]

Ten different singers, 20 different soundtracks for each singer (Indian, Bengali)

PLP

K-means

55.56

Eghbal-Zadeh et al. (2015) [18]

Artist20 (20 singers, 1413 songs)

MFCCs

KNN

84.31

Xing (2017) [48]

Ten different singers, 10 different music for each singer

LPC

GMM

81.8

Shen et al. (2019) [1]

MIR-1K dataset

MFCCs

LSTM

88.4

Loni and Subbaraman (2019) [17]

Twenty-six different singers, 550 different songs (Indian)

Formants, vibrato, timbre, and harmonic spectral envelope

SVM

86

Murthy et al. (2021) [8]

Indian popular singers’ database (IPSD), Artist20

MFCCs, LPCCs, SDCs, chroma, spectogram

YSA-RF-CNN

61.69–75.50

Noyum et al. (2021) [49]

Four different singers, 50 different songs for each singer

DWT

Linear

SVM

83.96

Costa et al. (2017) [29]

Latin Music Database, ISMIR 2004, and African music collection dataset

Spectrogram, RLBP, rhythm patterns (RP), statistical spectrum descriptors (SSD), and rhythm histograms (RH)

CNN

SVM

92

Li et al. (2021) [30]

Artist20, singer32 vs singer60

Spectrogram

CRNN

99.0–85.0

Nasrullah et al. (2019) [12]

Artist20

Spectrogram

CRNN

93.7 (F1)

Sharma et al. (2019) [31]

Artist20

MFCC

UBM

T-matrix

89.97

Zhang et al. (2022) [32]

Artist20

Mel-spectrogram, articulation, rhythmic complexity, rhythmic stability, dissonance, tonal stability, modality, x-vector

CRNN

81 (F1)

Proposed model

Nine different singers, 20 different songs for each singer

MFCC, octave-based spectral contrast

Extra Tree

89.4

Proposed model

Artist20

MFCC, octave-based spectral contrast

KNN

85.4