A Survey on: Sound Source Separation Methods

©2016, IJCERT All Rights Reserved DOI:10.22362/ijcert/2016/v3/i11/XXXX Page | 580
Volume 3, Issue 11, November-2016, pp. 580-584 ISSN (O): 2349-7084
International Journal of Computer Engineering In Research Trends
A Survey on: Sound Source Separation
Methods1
Ms. Monali R. Pimpale, 2
Prof. Shanthi Therese , 3
Prof. Vinayak Shinde,
1
Department of Computer Engineering, Mumbai University,
Shree L.R. Tiwari College of Engineering and Technology,Mira Road, India.
2
Department of Information Technology, Mumbai University,
Thadomal College of Engineering and Technology,Mumbai, India
3
Department of Information Technology, Mumbai University,
Shree L.R. Tiwari College of Engineering and Technology,Mira Road, India
Abstract— now a day’s multimedia databases are growing rapidly on large scale. For the effective management and
exploration of large amount of music data the technology of singer identification is developed. With the help of this
technology songs performed by particular singer can be clustered automatically. To improve the Performance of singer
identification the technologies are emerged that can separate the singing voice from music accompaniment. One of the
methods used for separating the singing voice from music accompaniment is non-negative matrix partial co factorization.
This paper studies the different techniques for separation of singing voice from music accompaniment.
Keywords—singer identification, non-negative matrix partial co factorization
——————————  ——————————
I.INTRODUCTION
The development of singer identification enables the
effective management of large amounts of music data.
With this singer identification technology, songs
performed by a particular singer can be automatically
clustered for easy management or searching. There are
many algorithms which are used for singer
identification which are based on the concept of
feature extraction which identifies the appropriate
singer from the obtained features. In popular music,
singing voice is combined with music accompaniment.
So those methods based on the features extracted
directly from the accompanied vocal segments are
difficult to acquire good performance when
accompaniment is stronger or singing voice is weaker.
To get better performance the techniques are emerged
which separates the singing voice from music
accompaniment. There are many sound source
separation algorithms which separates the singing
voice from music accompaniment. Sound source
separation means the tasks of evaluating the signal
produced by an individual sound source from a
mixture signal consisting of multiple sources. This is a
very fundamental problem in many audio signal
processing tasks, since analysis and processing of
isolated or single sources can be done with much
better accuracy than the processing of mixtures of
sounds. The term unsupervised learning is used to
characterize algorithms which try to separate and
learn the structure of sound sources in mixed data
based on information-theoretical principles, such as
statistical independence between sources, instead of
highly sophisticated modeling of the source
characteristics or human auditory perception. There
are many unsupervised learning sound source
separation algorithm some of them are independent
component analysis (ICA), sparse coding, and non-
Available online at: www.ijcert.org

Monali R. Pimpale," A Survey on: Sound Source Separation Methods”, International Journal of Computer Engineering In
Research Trends, 3(11):580-584,November-2016.DOI:10.22362/ijcert/2016/v3/i11/XXXX.
negative matrix factorization, which has been
tremendously used in source separation tasks in
several application areas [1].
II.SOUND SOURCE SEPARATION
Source separation is process in which several signals
are mixed together to form a combined signal and the
objective of source separation is to obtain or recover
the original component signals from the mixed or
combined signal. This is fundamental problem in
many audio signal processing tasks, because analysis
and processing of isolated sources can be done with
better accuracy than the processing of mixtures of
sounds.
The well-known example of a source separation is
the cocktail party problem, where a multiple people
are talking simultaneously in a room (for eg, in
cocktail party), and an individual who performs the
role of listener is trying to follow one of the
discussions. The human brain is capable to handle this
type of auditory source separation problem, but it is
very difficult problem to be solved in digital signal
processing. Several approaches have been proposed
for solving this problem but development is currently
still very much in progress.
III.SUPERVISED LEARNING
Supervised leaning is the type of machine learning
algorithm which uses known dataset which is also
referred as training data for making the predictions.
The training dataset includes input values and
corresponding response values. Size of training dataset
decides predictive power of the model.
In supervised learning model is prepared through a
training process where the model is required to make
predictions and is corrected when the predictions are
wrong. The training process continues until the model
achieves a desired level of accuracy on the training
data. Supervised learning includes three categories of
algorithm:
A. Classification
When the data are being used in the process of
category prediction, supervised learning is also called
classification. When there are only two choices, the
learning is called two-class or binomial classification.
When there are more than two categories, then this
problem is known as multi-class classification.
B. Support Vector Machine (SVM)
The support Vector Machine (SVM) was mainly
attracted a high degree of interest in the machine
learning research community. Support Vector Machine
is a supervised learning method used for classification.
SVM simultaneously work on minimization of the
imperial classification error and maximization of the
geometric margin. For this reason SVM called
maximum margin classifiers. Data is classified by
using the hyperplane. Sample along the hyperplane is
called Support Vector (SV). The separating hyperplane
is defined as the hyerplane that maximize distance
between the two parallel hyperplanes. If distance or
margin between parallel hyperplane is better than
SVM gives good classification.
Disadvantage: Most serious problem with SVMs is the
high algorithmic complexity and the extensive
memory requirements of the required quadratic
programming in large tasks. They can be abysmally
slow in test phase. This performs poorly on songs
where much of the frequency distribution of the
background is close to the vocal range [17].
C. Gaussian mixture model (GMM)
Gaussian mixture model (GMM) is used as a classifier
for the classification of the voice and unvoiced signal.
Gaussian mixture model (GMM) is a mixture of
several Gaussian distribution and therefore represent
different subclasses inside one big class. GMM to
represent perfectly the data distribution: the most
important thing for classification is to obtain a good
separator between the classes. This process of
classification was confirmed by considering
discriminative training of GMMs for classification.
Gaussian mixture model (GMM) is supervised
learning which is best work on the maximum
likelihood (ML) estimation using expectation
maximization (EM). If we compare traditional GMM
with pseudo GMM the nonlinear maps have better
performance on nonlinear problems, while the
computational complexity is almost the same as the
Expectation-Maximization (EM) algorithm for
traditional GMM according to the iteration
procedures. In the training phase, a music database
with manual vocal/nonvocal transcriptions is used to
form two separate GMM: a vocal GMM and second
nonvocal GMM. The expectation maximization (EM)
algorithm is an iterative method for calculating
maximum likelihood distribution parameter estimates

from incomplete data. EM algorithm is high for two
major reasons as similar to other kernel based
methods, it have to calculate kernel function for each
sample-pair over training set and in order to get the
largest Eigen value [16].
Disadvantages -This requires large set of Gaussian
functions with GMMs and also gives poor
performance.
D. Regression.
When a value is being predicted the supervised
learning is called regression. It is used to estimate real
values such as cost of houses, total sales etc. based on
continuous variables like total sale and stock prices
etc.
E. Anomaly detection
In many cases the goal is to identify data points or
categories that are simply unusual. The possible
variations are so numerous and the training examples
are so few, in such cases it is not feasible to learn what
and how fraudulent activity looks like. The anomaly
detection took the approach which simply learn how
normal activity looks like (using a history non-
fraudulent transactions) and identify the things that
are significantly different
IV.UNSUPERVISED LEARNING
Unsupervised learning is type of machine learning
algorithm to make presumption from dataset
consisting of input data without responses.
Unsupervised machine learning technique is not
provided with accurate results during training data. It
finds hidden clusters in input data sets which assist it
in getting the right results. Unsupervised learning
refers to the problem which finds hidden structure in
unlabeled data.
A. Computational Auditory Stream Analysis (CASA):
CASA methods are based on the ability of humans to
catch the sound and recognize individual sound
sources in a mixture of sound are referred to as
auditory scene analysis. Computational models of this
function mainly consist of two main stages. In First
stage, the mixture signal is decomposed into its
elementary time-frequency components. Then in
second stage, these time frequency components are
organized and grouped to their respective sound
sources. Our brain does not resynthesize or separate
the acoustic waveforms of each source separately; still
the human auditory system is a useful reference in the
development of one-channel sound source separation
systems, as it is the only existing system which can
robustly separate sound sources in different
circumstances.
Disadvantage: The performance of current CASA
system is still limited by pitch estimation errors and
residual noise.
B. Beamforming:
Beamforming achieves sound separation by using the
principle of spatial filtering. The focus of beamforming
is to magnify the signal coming from a specific
direction by a suitable configuration of a microphone
array at the same time the signals coming from other
directions are rejected. As the number of microphones
and the array length increases the amount of noise
attenuation increases. With a properly configured
array, beamforming can achieve high-quality
separation.
Disadvantage: The amount of noise attenuation
increases as the number of microphones.
C. ICA (Independent Component Analysis):
ICA is method for separating multivariate
(multidimensional) signal into its subcomponents. ICA
assumes thee subcomponents of multivariate signal
are independent of each other and they are non-
Gaussian signals. ICA has been usually used in
various ‘blind’ source separation tasks, where no or
little prior information is available about the source
signals. ICA has two assumptions:
1.The sound source signal are independent of each
other.
2.The values in each source signal have non Gaussian
distribution[12][13].
Disadvantage: A key and primary issue of this
method is before an effective source separation the
system should estimate the number of unknown
sources from the mixed signals.
D. Pitch Estimation and Tracking
Pitch is a perceptual property that allows the ordering
of sounds on a frequency-related scale. The technical
term for this property is fundamental frequency
(f0).Along with duration, loudness, and timbre pitch is
also a major auditory attribute of musical tones. Pitch
may be quantiﬁed as a frequency, but pitch is not a
purely objective physical property; it is a subjective
psychoacoustical attribute of sound. Historically, the
study of pitch and pitch perception has been a central
problem in psychoacoustics (i.e. the scientific study of
sound perception). The estimation of pitch is highly

related to source separation in the field of music. Many
music source separation methods use pitch estimation
as a previous step. Additionally pitch estimation
approaches often share the same techniques as those of
source separation. In the field of pitch estimation
several tasks are often diffierentiated. Monophonic
pitch estimation consists in estimating the pitch line of
an audio recording where a single pitched sound is
present at any given time.
Predominant pitch, bass line or melody estimation
often refers to the estimation of one of the pitch lines
in a polyphonic recording, where the selection of the
pitch line depends on the application. Multiple pitch
estimation consists in extracting all the pitch lines in a
polyphonic recording. These two last families of
methods are the ones of interest in the field of source
separation [6][7].
Disadvantages: Estimated fundamental frequency of
singing is difficult to be very accurate because of the
influence of accompaniment. Even if the estimated
fundamental frequency is correct the extracted
harmonics of singing voice are not completely pure
because the some harmonics components of singing
voice may be superimposed by pitched instrument.
E. Sparse coding
Sparse coding is class of unsupervised learning which
represents a mixture signal in terms of a small number
of active elements chosen out of a larger set. Sparse
coding is an efficient approach for learning structures
and separating sources from mixed data. Sparse
coding is a basic task in many fields including signal
processing, neuroscience and machine learning where
the goal is to learn a basis that enables a sparse
representation of one given set of data, if one exists.
Sparseness: The concept of sparse coding refers to
representation method where only few units are
effectively used to represent typical data vector. As
result of this most of the units takes values close to
zero while only few unit takes significantly non zero
values.The degree of sparseness is decided based on
the values of vector. If elements of vector are roughly
equally active then degree of sparseness is at low level.
If the most of elements take zero values on other hand
few of them take significant values then the degree of
sparseness is at high level [11][14].
F. Non-negative matrix factorization
Non negative matrix factorization (NMF) is a low-rank
approximation method where a nonnegative input
data matrix is approximated as a product of two non-
negative factor matrices. NMF has been used in
various applications, including image processing,
brain computer interface, document clustering,
collaborative predictions, and so on. NMF plays
important role in the sound source separation. The
algorithms based on non-negative matrix factorization
are robust and efficient for sound source separation
when the sources or components of signal are
dependent. NMF gives two output matrices one
contain the all vocal attribute and other matrix
indicates musical activities (i.e. musical notes).
Recent advances in matrix factorization methods
suggest collective matrix factorization or matrix co-
factorization to incorporate side information, where
several matrices (target and side information matrices)
are simultaneously decomposed, sharing some factor
matrices. Matrix co-factorization methods have been
developed to incorporate label information, link
information, and inter-subject variations [2][9].
Disadvantage – Imposes only the non-negativity
constraint.
G. Non negative matrix partial co factorization:
Many algorithms based on the non-negative matrix
factorization (NMF) were developed in applications
for blind or semi-blind source separation and those
NMF algorithms are efficient and robust for source
separation when sources are statistically dependent
under conditions that additional constraints are
imposed such as non-negativity, sparsity, smoothness,
lower complexity or better predictability. However,
without any prior knowledge of a source signal, the
standard NMF cannot separate specific source signal
from the mixing signal. To tackle this problem,
nonnegative matrix partial co-factorization (NMPCF)
was introduced. NMPCF is a joint matrix
decomposition integrating prior knowledge of singing
voice and accompaniment, to separate the mixture
signal into singing voice portion and accompaniment
portion. Matrix co-factorizations can be served as a
useful tool when side information matrices are
available, in addition to the target matrix to be
factorized. NMPCF was emerged from the concept of
joint decomposition or collective matrix factorization,
which make the multiple input matrices be
decomposed into several factor matrices while some of
them are shared, therefore, shows a greater potential
in singing voice separation from monaural
recordings[3] [4].

V.CONCLUSION
This paper presents different source separation
methods and also included NMPCF as new method
for source separation which gives better performance
than existing methods of source separation. So the
NMPCF can be used for singer identification with
better performance.
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A Survey on: Sound Source Separation Methods

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A Survey on: Sound Source Separation Methods