A tentative typology of audio source separation tasks - HAL

A tentative typology of audio source separation tasks
Emmanuel Vincent, Cédric Févotte, Rémi Gribonval, Laurent Benaroya,
Xavier Rodet, Axel Röbel, Eric Le Carpentier, Frédéric Bimbot
To cite this version:
Emmanuel Vincent, Cédric Févotte, Rémi Gribonval, Laurent Benaroya, Xavier Rodet, et al.. A
tentative typology of audio source separation tasks. 4th Int. Symp. on Independent Component
Analysis and Blind Signal Separation (ICA), Apr 2003, Nara, Japan. pp.715–720, 2003. <inria00544239>
HAL Id: inria-00544239
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4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), April 2003, Nara, Japan
Emmanuel Vincent
Xavier Rodet
Axel Röbel
Cédric Févotte
Éric Le Carpentier
IRCAM, Analysis-Synthesis Group
1, place Igor Stravinsky
1, rue de la Noë – BP 92 101
F-75004 PARIS
Rémi Gribonval
Laurent Benaroya
Frédéric Bimbot
Campus de Beaulieu
algorithms have to address different tasks. For example,
some applications require finding the number of sources
given the observations, and others require recovering the
source signals given the observations and the structure of
the mixing system.
We propose a preliminary step towards the construction of
a global evaluation framework for Blind Audio Source Separation (BASS) algorithms. BASS covers many potential
applications that involve a more restricted number of tasks.
An algorithm may perform well on some tasks and poorly
on others. Various factors affect the difficulty of each task
and the criteria that should be used to assess the performance of algorithms that try to address it. Thus a typology of BASS tasks would greatly help the building of an
evaluation framework. We describe some typical BASS applications and propose some qualitative criteria to evaluate
separation in each case. We then list some of the tasks to be
accomplished and present a possible classification scheme.
A given separation algorithm may perform well on some
tasks and poorly on others. Depending on the task, various
factors affect the difficulty of the separation, and distinct
criteria may be used to evaluate the performance of an algorithm, and compare it to other algorithms.
The first step to determine which task(s) a given separation algorithm may achieve is to list and classify some of
the interesting tasks. As tasks and applications are related,
this implies to list and classify typical applications of BASS
too. We attempt to address these questions in this paper. As
a further step, we propose in a companion paper [3] some
numerical criteria to evaluate the performance of BASS algorithms on some of these tasks.
Blind Audio Source Separation (BASS) has been a subject
of intense work during the latest years. Several models have
emerged, such as Independent Component Analysis (ICA)
[1] and Sparse Decompositions (SD) [2], and it is now more
or less well known how to solve the separation problem under these models with efficient and robust algorithms. However BASS is not just about solving some tractable model
(e.g. finding independent or sparse components), it is about
recovering results that make sense according to the target
In Section 2, we present two large classes of BASS applications. In Sections 3 and 4, we give some examples
among each class and we identify some candidate qualitative criteria to measure separation quality and separation
difficulty. In Section 5, we list some of the tasks to be addressed by BASS algorithms and we present a possible typology.
BASS covers many applications, such as high quality
separation of musical sources, signal/speech enhancement,
multimedia documents indexing, speech recognition in a
“cocktail party” environment or source localization for auditory scene analysis. Depending on the application, BASS
Let us emphasize that this paper should be considered
as a preliminary proposal that does not contain final results but rather presents some thoughts on the definition,
the typology and the evaluation of BASS tasks. We hope
the source separation community will consider these topics more closely, so that the construction of an agreed-upon
evaluation framework for BASS algorithms will become possible.
This work is part of a Junior Researchers Project funded by GdR ISIS
(CNRS). See http://www.ircam.fr/anasyn/ISIS/ for some insights on the Project.
An important distinction that can be made among BASS applications is whether the output of the algorithm is a set
of extracted sources that are intended to be listened to or
not. We term these two categories Audio Quality Oriented
(AQO) and Significance Oriented (SO) applications.
AQO applications extract sources that are listened to,
straight after separation or after some post-processing audio
treatment. Most of the literature focuses on this goal by using ICA- and SD-related methods (see [4] for a review of
ICA methods applied to audio signals). Some criteria for
separation quality and separation difficulty have been proposed in [5, 6], and we propose some others in our companion paper [3].
3.1. One versus all
The one versus all problem consists in extracting one sort
of sound (the target source ) from a mixture. Generally
the other sources are considered as noise.
Some examples include restoration of old monophonic
musical recordings [7], speech de-noising and de-reverberation for auditory protheses or mobile phones [8] and extraction of some interesting sounds in a polyphonic musical
excerpt for electronic music creation.
In this context, a “good” separation requires estimating
the source with a high Signal to Noise Ratio (SNR). The
SNR criterion can be modified to model the specificities of
hearing, such as masking phenomena, as does the criterion
introduced in [9]. Other criteria can be used to evaluate the
separation quality when an exact estimation is not needed.
This is often the case when indeterminacies arise due to convolutive mixing. For some applications, it may be sufficient
to recover some filtered versions of the target source and
not itself [10, 11]. The “naturalness” of these versions
could be measured by criteria like timbre distortion [12] or
comparisons with a database of room impulse responses.
For other applications, one may wish to extract the contribution of to each sensor [13], that is to say to estimate the
multichannel signal . In
such cases, quality criteria may have to take into account
the difference between the perceived spatial direction of when listening to and to .
The problem of extracting several sources in order to
listen to them separately falls in the one versus all category
too. For each source, a global SNR can be computed and
can be decomposed into the contributions of crosstalk (remainder of the other sources), additive noise and algorithmic artifacts [3].
In SO applications, the extracted sources and/or mixing parameters are processed to obtain information at more
abstract levels, in order to find a representation of the observations related to human perception. For instance, looking
for the number and the kind of the instruments in a musical
excerpt enters the scope of SO separation. Separation quality criteria are generally less demanding than in AQO applications because the aim of SO separation is only to keep
specific features of the sources. Thus, a rough separation
may be sufficient (possibly with high distortion), depending on the robustness of the subsequent feature extraction
An important remark is that separation and information
extraction do not have to be separated processes. For example, the auditory system uses a priori and contextual information to perform separation, which means recognition can
help separation.
For the sake of clarity, let us define the few notations
used in the rest of this paper. The general (possibly convolutive) BASS problem is expressed
using the matrix of filters formalism as , where is the vector of the sources , is the vector of the
observations , is the matrix of mixing filters.
Note that we limit ourselves to linear time-invariant mixing
systems. A similar analysis could be carried out extending
the model to non-linear time-variant systems to take into account the dynamic compression applied to radio broadcasts
or the spatial movements of the sources for example.
The one versus all problem may be tackled at various
difficulty levels. The algorithms are influenced by the number of sources, the number of sensors, the noise level, the
dependency between the sources, the kind of mixing (instantaneous vs convolutive), etc. The blind case is usually
addressed by ICA [1] and SD [2]. Other algorithms can handle a priori information, like a model of the playing musical
instrument and its musical score used in [14] or the video of
the lips corresponding to a noisy speech signal used in [8].
Within AQO applications, we can distinguish two major
families of applications. The first category is related to
applications where we are interested in each individual extracted source, while the second one corresponds to applications where the goal is to listen to a new mixture of the
3.2. Audio scene modification
Audio scene modification consists in obtaining a new mixture , for example by
extracting all the sources from the original observations , applying an adapted audio processing to each
source and remixing the tracks using a possibly different
mixing matrix , in order to listen to the result . Let
us note that prior extraction of each source is not a requirement in such applications, it is only a convenient way to
describe the desired result and a possible way to achieve it.
The purpose of finding an exhaustive description of a complex audio scene is called Auditory Scene Analysis (ASA)
[18]. Most SO applications can be seen as by-products of
The main applications of SO separation concern the indexing of audiovisual databases and the construction of intelligent hearing systems. Depending on the application,
one may need low level descriptive elements, high level
ones or both. Some examples of descriptive elements are the
score of each instrument in a musical excerpt [19], the text
pronounced by a speaker in a noisy environment [20, 21],
or the spatial position of the sources w.r.t. the sensors in a
“real world” recording [22], etc. Other descriptions consist
in telling the name of each instrument and the musical genre
[23], identifying the speaker [24], linking audio sources and
corresponding visual objects on a video [25], etc.
Examples include re-mastering of a stereo CD, blind
multichannel diffusion of stereo recordings [15], spatial interpolation [16] and cancellation of the voice in a song for
“automatic karaoke”.
Evaluation of the separation results may rely on calculating the SNR of the estimated remixed scene w.r.t. the expected result (that is to say the scene constructed by remixing the true sources). Depending on the signal “zones” affected by post-processing and remixing, quality criteria may
be a little less restrictive than for the one versus all problem.
For example, when the purpose is to increase slightly the
“presence” of an instrument in a CD, distortion or crosstalk
in the extracted instrument won’t account for much in the
final result. Indeed, the other sources will most likely mask
the zones containing crosstalk after remixing, and even larger zones since auditory masking effects usually come into
play [9].
The purpose of S0 separation is to preserve as much as
possible the features used to compute the descriptive elements. To evaluate the quality of a global description consisting of many descriptive elements, the quality of each element is first evaluated separately by a distinct criterion. The
quality of continuous-valued descriptive parameters, such
as the positions of the sources, is measured by simple distances. The evaluation of discrete-valued descriptive parameters, such as the name of the instrument is done by
calculating misclassification or recognition error rates [26].
The quality of the whole audio description may then be
expressed by a weighted combination of all these criteria.
When such a weighting is hard to choose objectively, it may
be preferable to conduct a series of listening tests to obtain
a global separation/description grade [27, 18].
Difficulties encountered by the algorithms include those
of the one versus all problem : they are influenced by the
number of sources, the number of sensors, etc. But the
amount of change introduced by the intermediate audio processing and the new number of channels obtained after remixing with also play a role. For instance, given
a mono recording, it is more difficult to cancel one of its
sources or to broadcast each of its sources on one or more
channel(s) than to augment slightly the volume of one of
its sources. Like in the one versus all problem, algorithms
can also use a priori information or not. The use of such
information is to determine satisfactory and to achieve
the desired effect. In blind remixing, this can only be done
by relying on directly computable features, such as the direction or the instantaneous power of the sources. When
models of the sources are available, it becomes conceivable
to name each source (i.e. to label it with the right model),
so that it can undergo more specific treatments. One can
think of raising the level of “the voice” in a recording if a
model of “voice” is available.
Criteria for the separation difficulty depend on the application. The number of sensors and their selectivity and
the amount of reverberation in the environment affect the
retrieval of the mixing parameters. Source classification is
more or less difficult according to the number of classes to
recognize and the robustness of the features calculation. For
some applications, a real-time constraint is also needed.
As we have shown, AQO and SO separation are used in
many different applications, each one having its own evaluation criteria. However, these applications correspond to a
smaller number of tasks to be accomplished by BASS algorithms, depending on the relevant objects in the model, the
kind of mixing and the amount of available information.
SO applications aim at retrieving source features and/or mixing parameters to describe complex audio signals at various
cognitive levels, focusing on different aspects of sound [17].
A task is specified by the nature of the objects that the
algorithm takes as an input, the nature of its output, and
Blind mixing
structure of
(not always)
Blind source
structure of Blind remixing
structure of ,
generic and
number of
sources following Identification
model of description
of and Source
of and or model+description
of and ,
adapted and
source” or “the leftmost source” on a stereo recording. In
Table 1, this is denoted by ’generic’ B and f.
We tried to choose the names of the tasks in correspondence to what is used in the literature. For example, the
Blind Mixing Identification task contains as a special case
what is usually called Blind System Identification [28]. The
Detection task is close to the Verification problem in speaker
recognition [29] and to the Classification problem in audiovisual database indexing. The Remixing task includes
the Cancellation problem, which consists in cancelling one
source in the mixture.
or The main distinction we propose between tasks is whether models of the sources (generally learned from a database of samples of the sources) are available or not. The
difference between blind tasks and their semi-blind “counterparts” is indeed quite important.
However, contrary to other contributions concerning the
subject [5, 6], we group in each task the instantaneous mixing case and the convolutive one. In fact, we believe the
various possible structures of should be considered as
various difficulty criteria (or subtasks) for the same overall
task, rather than separate tasks. By structure of , we mean
information such as the number of sources or the length of
the mixing filters (simple gain, gain-delay, short FIR, IIR
with few parameters). For some tasks, this structure is given
as input to the algorithms.
For some non blind tasks, we also group the problems
where a model of the sources is given and those where a
description of the sources is also available. The term model
covers all sorts of general signal models, such as the hidden Markov models used in [8], the modified additive models used in [12] or even a physical model of the source instrument [30]. Source models can also contain learned information about source interaction, for example parameters
describing the degree of independence between them [31].
There can also be models of the mixing system. In most
cases, the definition of a task does not include a specific
type of model that an algorithm can rely on in order to solve
the task. Generally, the algorithm is trained –prior to running on “live” data– using some training samples of each
source. In this context, a description may be any kind of
knowledge that restricts the models depending on the particular piece of signal considered : a temporal segmentation, a musical score, the size of the recording room. As for
the distinction between instantaneous and convolutive problems, we believe that giving or not this kind of descriptions
to the algorithm is facing different difficulty levels inside an
overall task, but not separate tasks. This puts together rather
different problems, for example extracting a piano and a violin playing together with the only information that there
are a piano and a violin, or performing the same extraction
Table 1. Some BASS tasks (see Section 5 for comments
and previous Sections for notations)
a qualitative description of how the quality of the output
should be assessed.
Table 1 lists some tasks according to the input-output
scheme of the algorithms using the notations of the previous
Sections. In the ’Input’ column, we only list what comes in
addition to the observations . The (qualitative) evaluation
criteria are implicit, but it is indeed a crucial step to define
relevant and agreed upon procedures to evaluate the performance of an algorithm on a task.
Note that for some tasks the well-known indeterminacies of the BASS problem are explicitly expressed in the
description of the output, using a permutation matrix and
a diagonal matrix of filters , or using the different delimiters and for ordered and unordered sets.
For the remixing task, the input include the new mixing
matrix and audio processing to perform on each source. In
the non-blind case, it is conceivable to specify this intrinsically so that a given processing is performed on a source
identified by some model (“the piano” for example). This is
denoted by ’adapted’ B and f. However, in the blind situation, it seems very hard to specify the nature of the source
that should undergo a given audio processing. One may thus
be restricted to specifying a source in terms of “the loudest
knowing their scores. However, these are the extreme cases
among many intermediate assumptions. Sometimes, only
one of the sources is learned and only an imperfect score is
available, like in [14].
under-determined problems. Other tasks require the design
of other relevant numerical criteria.
A database structure and some labeled test signals are
also readily available online [32].
Finally, it seems that some BASS applications such as
audio scene modification have been less studied than the one
versus all problem for instance, despite their generally less
demanding requirements. We hope this work will trigger
interest for new research goals.
In this paper, we described some of the most typical applications encountered in the field of BASS, and we proposed
to group these applications into two main categories : AQO
(Audio Quality Oriented) and SO (Significance Oriented)
separation. AQO applications aim at extracting sources for a
listening purpose, whereas in SO applications the extracted
sources are used for classification and description.
For each application, we stressed some of the audio specificities to be taken into account when designing related
BASS algorithms. We proposed some qualitative criteria to
evaluate the performance of a given algorithm for the application, and the difficulty of the application itself depending
on various factors such as the available amount of prior information, the noise level, the instantaneous or convolutive
nature of the mixtures, etc.
This lead us to propose a tentative typology of the corresponding tasks to be solved by BASS algorithms, according to the input-output scheme of the algorithms. The main
classification axis is the distinction between blind and non
blind tasks. We retained the classical distinction between
instantaneous and convolutive mixing as different levels of
difficulty for a given task.
This work has been performed within a Junior Researchers
Project “Resources for Audio Signal Separation” funded by
GdR ISIS (CNRS). The goal of the project is to identify
the specificities of audio signal separation, to suggest relevant numerical criteria to evaluate separation quality, and to
gather test signals of calibrated difficulty level, in order to
evaluate the performance of existing and future algorithms.
Some evaluation routines, a database of audio signals
and a discussion list can be found on the web-site [32].
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BASS tasks typology and evaluation or related topics using
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