Singing Voice Identification Using Spectral Envelope Estimation

Bartsch, Mark A.; Wakefield, Gregory H.

doi:10.1109/tsa.2003.822637

Cited by 36 publications

(20 citation statements)

References 23 publications

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“…An average error rate of 16.2 % is achieved in segment level identification. In [10], composite transfer function-based features are extracted and polynomial classifier is used for classification. Self-recorded database for 12 female singers are used to build training model which produces 82 % accuracy.…”

Section: Related Workmentioning

confidence: 99%

Singer identification using perceptual features and cepstral coefficients of an audio signal from Indian video songs

Ratanpara

Patel²

2015

J AUDIO SPEECH MUSIC PROC.

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Singer identification is a difficult topic in music information retrieval because background instrumental music is included with singing voice which reduces performance of a system. One of the main disadvantages of the existing system is vocals and instrumental are separated manually and only vocals are used to build training model. The research presented in this paper automatically recognize a singer without separating instrumental and singing sounds using audio features like timbre coefficients, pitch class, mel frequency cepstral coefficients (MFCC), linear predictive coding (LPC) coefficients, and loudness of an audio signal from Indian video songs (IVS). Initially, various IVS of distinct playback singers (PS) are collected. After that, 53 audio features (12 dimensional timbre audio feature vectors, 12 pitch classes, 13 MFCC coefficients, 13 LPC coefficients, and 3 loudness feature vector of an audio signal) are extracted from each segment. Dimension of extracted audio features is reduced using principal component analysis (PCA) method. Playback singer model (PSM) is trained using multiclass classification algorithms like back propagation, AdaBoost.M2, k-nearest neighbor (KNN) algorithm, naïve Bayes classifier (NBC), and Gaussian mixture model (GMM). The proposed approach is tested on various combinations of dataset and different combinations of audio feature vectors with various Indian male and female PS's songs.

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Section: Related Workmentioning

confidence: 99%

Singer identification using perceptual features and cepstral coefficients of an audio signal from Indian video songs

Ratanpara

Patel²

2015

J AUDIO SPEECH MUSIC PROC.

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“…With the augmentation of the error metric based on the DFB features, the overall verification and identification rule combining the average geometric distance map error (E MAP ) with DFB error (E DFB ) is (12) whereẼ is the combined error and β is factor between 0 and 1. The β takes a value equal to, greater or less than 0.5, depending on which error factor is favored.…”

Section: Verification and Identification Of A Query Fingerprintmentioning

confidence: 99%

“…(12). Matching scores resulting from the genuine matches and the imposter matches were collected to plot their histogram distributions.…”

Section: Fingerprint Verification Based On Combined Featuresmentioning

confidence: 99%

“…A number of biometric technologies have been developed based on diverse biometric cues, such as DNA [1], ear [2], face [3], fingerprint [4], gait [5], hand and finger geometry [6], iris [7], keystroke [8], odor [9], palm print [10], hand writing and signature [11], and voice [12]. These cues showed their usefulness in various applications, ranging from Internet access and computer system security, secure electronic access, passport control, banking, mobile phone, credit card fraud protection, secure access to building, health and social services, parenthood lineage determination, antiterrorist, and forensics.…”

Section: Introductionmentioning

confidence: 99%

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Fingerprint Verification and Identification Based on Local Geometric Invariants Constructed from Minutiae Points and Augmented with Global Directional Filterbank Features

Pintavirooj

Cohen

Iampa

2014

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper addresses the problems of fingerprint identification and verification when a query fingerprint is taken under conditions that differ from those under which the fingerprint of the same person stored in a database was constructed. This occurs when using a different fingerprint scanner with a different pressure, resulting in a fingerprint impression that is smeared and distorted in accordance with a geometric transformation (e.g., affine or even non-linear). Minutiae points on a query fingerprint are matched and aligned to those on one of the fingerprints in the database, using a set of absolute invariants constructed from the shape and/or size of minutiae triangles depending on the assumed map. Once the best candidate match is declared and the corresponding minutiae points are flagged, the query fingerprint image is warped against the candidate fingerprint image in accordance with the estimated warping map. An identification/verification cost function using a combination of distance map and global directional filterbank (DFB) features is then utilized to verify and identify a query fingerprint against candidate fingerprint(s). Performance of the algorithm yields an area of 0.99967 (perfect classification is a value of 1) under the receiver operating characteristic (ROC) curve based on a database consisting of a total of 1680 fingerprint images captured from 240 fingers. The average probability of error was found to be 0.713%. Our algorithm also yields the smallest false non-match rate (FNMR) for a comparable false match rate (FMR) when compared to the well-known technique of DFB features and triangulation-based matching integrated with modeling non-linear deformation. This work represents an advance in resolving the fingerprint identification problem beyond the state-of-the-art approaches in both performance and robustness.

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“…Penelitian [9] mengusulkan klasifikasi suara dengan memanfaatkan metode Gaussian Mixture Model dan Kuantisasi Vektor. Sedangkan [10] dan [11] berturut-turut melakukan identifikasi suara menggunakan metode Spectral Envelope Estimation Artificial Neural Network. Namun deteksi yang dilakukan tersebut belum dikombinasikan dengan aplikasi pemutar lagu.…”

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Sistem Rekomendasi Lagu Berdasarkan Jenis Suara Penyanyi Pada Aplikasi Karaoke

Ahmadiyah¹,

Sunaryono²,

Ramadhan³

2017

JUTI

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ABSTRAKI. PENDAHULUAN ARAOKE yang dalam bahasa Jepang berarti orkestra kosong [1] merupakan salah satu sarana hiburan yang banyak diminati oleh masyarakat. Sejak kemunculannya di awal 1970an, saat ini karaoke telah mengalami banyak perkembangan dan hadir dalam berbagai platform seperti dekstop, web dan perangkat bergerak. Fitur utama yang pasti ada adalah kumpulan lagu tanpa suara penyanyi dan navigasi pemutar lagu. Fitur aksesoris lain dapat berupa penilaian, kolaborasi dengan penyanyi lain dll.Pengguna karaoke pun memiliki kemampuan musik yang bervariasi, mulai dari yang penyuka musik yang kurang memahami tinggi rendah nada hingga penyanyi profesional yang sudah memahami karakter suara yang dimiliki.Bagi pengguna karaoke yang kurang paham tentang karakter suara atau nada kadang timbul permasalahan yaitu lagu yang dipilih tidak sesuai dengan jenis suara penyanyi tersebut, sehingga penyanyi menyanyi dengan nada terlalu tinggi atau terlalu rendah dari nada yang seharusnya sehingga menimbulkan suara fals. Permasalahan ini belum ditangani oleh aplikasi karaoke yang ada saat ini. Maka dibutuhkan sebuah aplikasi karaoke yang dapat K

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Singing Voice Identification Using Spectral Envelope Estimation

Cited by 36 publications

References 23 publications

Singer identification using perceptual features and cepstral coefficients of an audio signal from Indian video songs

Singer identification using perceptual features and cepstral coefficients of an audio signal from Indian video songs

Fingerprint Verification and Identification Based on Local Geometric Invariants Constructed from Minutiae Points and Augmented with Global Directional Filterbank Features

Sistem Rekomendasi Lagu Berdasarkan Jenis Suara Penyanyi Pada Aplikasi Karaoke

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