Singing-voice separation from monaural recordings using robust principal component analysis

Huang, Po-Sen; Chen, Scott Deeann; Smaragdis, Paris; Hasegawa‐Johnson, Mark

doi:10.1109/icassp.2012.6287816

Cited by 294 publications

(257 citation statements)

References 8 publications

Supporting

Mentioning

257

Contrasting

Order By: Relevance

“…For better separation outcomes masking can be applied to the separation results of ALM that are low rank A and sparse E matrices by using binary time frequency masking [6]. We need to accurately segregate the components as singing voice mostly lines the music accompaniment during beat instances in order to match with rhythmic structure of the song and hence we apply masking for enhanced separation outcomes.…”

Section: Fig1 Proposed Systemmentioning

confidence: 99%

“…1 denote the L1-norm that is the sum of the absolute values of matrix entriesis an valuable surrogate for L0 psuedo norm, the number of non-zero entries in the matrix. is the trade off parameter between the rank of A and sparsity of E [6].…”

Section: Fig1 Proposed Systemmentioning

confidence: 99%

“…But PCA is subtle to outliners and performance declines under bulky corruption. To solve this issue,robust PCA (RPCA) [2,6] is used to render PCA robust to outliners and gross corruption. A data matrix M∈ × can be uniquely and exactly be decomposed into a low rank component A and a sparse component E,also retrieval of low rank matrix by convex programming.…”

Section: Fig1 Proposed Systemmentioning

confidence: 99%

See 2 more Smart Citations

Singing Voice separation from Polyphonic Music Accompanient using Compositional Model

Umap¹,

Chaudhari²

2015

IJAREEIE

View full text Add to dashboard Cite

There are abundant real time applications for singing voice separation from mixed audio. By means of Robust Principal Component Analysis (RPCA) which is a compositional model for segregation, which decomposes the mixed source audio signal into low rank and sparse components, where it is presumed that musical accompaniment as low rank subspace since musical signal model is repetitive in character while singing voices can be treated as moderately sparse in nature within the song. We propose an efficient optimization algorithm called as Augmented lagrange Multiplier designed to solve robust low dimensional projections. Performance evaluation of the system is verified with the help of performance measurement parameter such as source to distortion ratio(SDR),source to artifact ratio(SAR), source to interference ratio(SIR) and Global Normalized source to Distortion Ratio (GNSDR).KEYWORDS: Robust Principle Component Analysis (RPCA),Singing Voice Separation, Augmented Lagrange Multiplier (ALM),low rank matrix,sparse matrix. I.INTRODUCTIONNumerous classes of information are composed as constructive mixtures of portions. Constructive combination, is additive combination that do not result in deduction or diminishment of any of the portion of information, this is referred to as "compositional" data. To characterize such information, various mathematical models are developed. Such models have provided new standards to solve audio processing problems, such as blind and supervised source separation and robust recognition. Compositional models are used in audio processing systems to advance the state of the art on many difficulties that deal with audio data involving of multiple sources, for example on the analysis of polyphonic music and recognition of noisy speech. Thus we use robust principal component analysis (RPCA) as a compositional model in this paper.Robust Principal Component Analysis (RPCA) method is extensively used in the field of image processing for image segmentation, surveillance video processing, batch image alignment etc. This procedure has received recent prominence in the field of audio separation for the application of singer identification, musical information retrieval, lyric recognition and alignment.A song usually comprises of mixture of human vocal and musical instrumental audio pieces from string and percussion instruments etc. Our area of interest is segregating vocal line from music which is complex and vital musical signal element from song, thus we can treat musical as intrusion or noise with respect to singing voice. Human auditory system has incredible potential in splitting singing voices from background music accompaniment. This task is natural and effortless for humans, but it turns out to be difficult for machines [15].Compositional model based RPCA has emerged as a potential method for singing voice separation based on the notion that low rank subspace can be assumed to comprise of repetitive musical accompaniment, whereas the singing voice is relatively sparse in time frequency...

show abstract

Section: Fig1 Proposed Systemmentioning

confidence: 99%

Section: Fig1 Proposed Systemmentioning

confidence: 99%

Section: Fig1 Proposed Systemmentioning

confidence: 99%

See 1 more Smart Citation

Singing Voice separation from Polyphonic Music Accompanient using Compositional Model

Umap¹,

Chaudhari²

2015

IJAREEIE

View full text Add to dashboard Cite

show abstract

“… The principal component analysis method (PCA) extracts the principal component by a linear transformation, computed using singular value decomposition algorithms [13]. …”

Section: A Dimension Reductionmentioning

confidence: 99%

Voice Detection in Traditionnal Tunisian Music using Audio Features and Supervised Learning Algorithms

Ziadi¹,

Amiri²

2018

ijacsa

View full text Add to dashboard Cite

Abstract-The research presented in this paper aims to automatically detect the singing voice in traditional Tunisian music, taking into account the main characteristics of the sound of the voice in this particular music style. This means creating the possibility to automatically identify instrumental and singing sounds. Therefore different methods for the automatic classification of sounds using supervised learning algorithms were compared and evaluated. The research is divided into four successive stages. First, the extraction of features vectors from the audio tracks (through calculation of the parameters of sound perception) followed by the selection and transformation process of relevant features for singing/instrumental discrimination. Then, using learning algorithms, the instrumental and vocal classes were modeled from a manually annotated database. Finally, the evaluation of the decision-making process (indexing) was applied on the test part of the database. The musical databases used for this study consists of extracts from the national sound archives of Centre of Mediterranean and Arabic Music (CMAM) and recordings made especially for this research. The possibility to index audio data (classify/segment) into vocal and instrumental recognition allows for the retrieval of content-based information of musical databases.

show abstract

“…The RPCP and its variants have found various promising applications, particularly in image and signal processing; e.g. video surveillance [10], face recognition [17], texture modeling [35], video inpainting [16], audio separation [15], latent semantic indexing [25], etc.…”

Section: Introductionmentioning

confidence: 99%

Robust Principal Component Pursuit via Inexact Alternating Minimization on Matrix Manifolds

Hintermüller

2014

J Math Imaging Vis

View full text Add to dashboard Cite

Robust principal component pursuit (RPCP) refers to a decomposition of a data matrix into a low-rank component and a sparse component. In this work, instead of invoking a convex-relaxation model based on the nuclear norm and the 1 -norm as is typically done in this context, RPCP is solved by considering a least-squares problem subject to rank and cardinality constraints. An inexact alternating minimization scheme, with guaranteed global convergence, is employed to solve the resulting constrained minimization problem. In particular, the low-rank matrix subproblem is resolved inexactly by a tailored Riemannian optimization technique, which favorably avoids singular value decompositions in full dimension. For the overall method, a corresponding q-linear convergence theory is established. The numerical experiments show that the newly proposed method compares competitively with a popular convex-relaxation based approach.

show abstract

Singing-voice separation from monaural recordings using robust principal component analysis

Cited by 294 publications

References 8 publications

Singing Voice separation from Polyphonic Music Accompanient using Compositional Model

Singing Voice separation from Polyphonic Music Accompanient using Compositional Model

Voice Detection in Traditionnal Tunisian Music using Audio Features and Supervised Learning Algorithms

Robust Principal Component Pursuit via Inexact Alternating Minimization on Matrix Manifolds

Contact Info

Product

Resources

About