## Section: New Results

### Source separation and localization

Source separation, sparse representations, tensor decompositions, semi-nonnegative independent component analysis, probabilistic model, source localization

Source separation is the task of retrieving the source signals underlying a multichannel mixture signal.

About a decade ago, state-of-the-art approaches consisted of representing the signals in the time-frequency domain and estimating the source coefficients by sparse decomposition in that basis. These approaches rely only on spatial cues, which are often not sufficient to discriminate the sources unambiguously. Over the last years, we proposed a general probabilistic framework for the joint exploitation of spatial and spectral cues [106] , which generalizes a number of existing techniques including our former study on spectral GMMs [61] . We showed how it could be used to quickly design new models adapted to the data at hand and estimate its parameters via the EM algorithm, and it became the basis of a large number of works in the field, including our own. In the last years, improvements were obtained through the use of prior knowledge about the source spatial covariance matrices [83] , [92] , [91] , knowledge on the source positions and room characteristics [84] , or a better initialization of parameters thanks to specific source localization techniques [67] . This accumulated progress lead to two main achievements last year: a new version of the Flexible Audio Source Separation Toolbox, fully reimplemented, was released [108] and we published an overview paper on recent and going research along the path of *guided* separation, *i.e.*, techniques and models allowing to incorporate knowledge in the process towards efficient and robust solutions to the audio source separation problem, in a special issue of IEEE Signal Processing Magazine devoted to source separation and its applications [113] .

#### Towards real-world separation and remixing applications

Participants : Nancy Bertin, Frédéric Bimbot, Nathan Souviraà-Labastie, Ewen Camberlein, Romain Lebarbenchon.

Main collaboration: Emmanuel Vincent (EPI PAROLE, Inria Nancy)

While some challenges remain, work from previous years and our review paper on guided source separation [113] highlighted that progress has been made and that audio source separation is closer than ever to successful industrial applications, especially when some knowledge can be incorporated. This was exemplified by the contract with MAIA Studio, which reached its end in December 2014 and showed in particular how user input or side information could raise source separation tools to efficient solutions in real-world applications.

In some applicative contexts of source separation, several mixtures are available which contain similar instances of a given source. We have designed a general multi-channel source separation framework where additional audio references are available for one (or more) source(s) of a given mixture. Each audio reference is another mixture which is supposed to contain at least one source similar to one of the target sources. Deformations between the sources of interest and their references are modeled in a linear manner using a generic formulation. This is done by adding transformation matrices to an excitation-filter model, hence affecting different axes, namely frequency, dictionary component or time. A nonnegative matrix co-factorization algorithm and a generalized expectation-maximization algorithm are used to estimate the parameters of the model. Different model parameterizations and different combinations of algorithms have been tested on music plus voice mixtures guided by music and/or voice references and on professionally-produced music recordings guided by cover references. Our algorithms has provided improvement to the signal-to-distortion ratio (SDR) of the sources with the lowest intensity by 9 to 15 decibels (dB) with respect to the original mixtures [25] . Combining these techniques, with automatic audio motif spotting, we have proposed a new concept called SPORES (for SPOtted Reference based Separation) and applied it to guided separation of audio tracks [13] .

This year saw the beginning of a new industrial collaboration, in the context of the VoiceHome project, aiming at another challenging real-world application: natural language dialog in home applications, such as control of domotic and multimedia devices. As a very noisy and reverberant environment, home is a particularly challenging target for source separation, used here as a pre-processing for speech recognition (and possibly with stronger interactions with voice activity detection or speaker identification tasks as well). In 2015, we participated in a data collection campaign, and in benchmarking and adaptation of existing localization and separation tools to the particular context of this application.

#### Implicit localization through audio-based control for robotics

Participant : Nancy Bertin.

Main collaborations (audio-based control for robotics): Aly Magassouba and François Chaumette (Inria, EPI LAGADIC, France)

Acoustic source localization is, in general, the problem of determining the spatial coordinates of one or several sound sources based on microphone recordings. This problem arises in many different fields (speech and sound enhancement, speech recognition, acoustic tomography, robotics, aeroacoustics...) and its resolution, beyond an interest in itself, can also be the key preamble to efficient source separation. Common techniques, including beamforming, only provides the *direction of arrival* of the sound, estimated from the *Time Difference of Arrival* (*TDOA*) [67] . This year, we have particularly investigated alternative approaches, either where the explicit localization is not needed (audio-based control of a robot) or, on the contrary, where the exact location of the source is needed and/or TDOA is irrelevant (cosparse modeling of the acoustic field, see Section
7.1.3 ).

In robotics, the use of aural perception has received recently a growing interest but still remains marginal in comparison to vision. Yet audio sensing is a valid alternative or complement to vision in robotics, for instance in homing tasks. Most existing works are based on the relative localization of a defined system with respect to a sound source, and the control scheme is generally designed separately from the localization system.

In contrast, the approach that we started investigating last year focuses on a sensor-based control approach. We proposed a new line of work, by considering the hearing sense as a direct and real-time input of closed loop control scheme for a robotic task. Thus, and unlike most previous works, this approach does not necessitate any explicit source localization: instead of solving the localization problem, we focus on developing an innovative modeling based on sound features. To address this objective, we placed ourselves in the sensor-based control framework, especially visual servoing (VS) that has been widely studied in the past [78] .

From now on, we have established an analytical model linking sound features and control input of the robot, defined and analyzed robotic homing tasks involving multiple sound sources, and validated the proposed approach by simulations and experiments with an actual robot. This work is mainly lead by Aly Magassouba, whose Ph.D. is co-supervised by Nancy Bertin and François Chaumette. A conference paper presenting these first results was published this year [34] and another was submitted to ICRA 2016. Future work will include additional real-world experiments with the robot Romeo from Aldebaran Robotics, investigation of new tasks with active sensing strategies, explicit use of echoes and reverberation to increase robustness, and exploration of dense methods (control from raw acoustic signals rather than from acoustic features).

#### Brain source localization

Participants : Laurent Albera, Srdan Kitic, Nancy Bertin, Rémi Gribonval.

Main collaborations : Hanna Becker (GIPSA & LTSI, France), Pierre Comon (GIPSA, France), Isabelle Merlet (LTSI, France), Fabrice Wendling (LTSI, France)

**From tensor to sparse models**

The brain source imaging problem has been widely studied during the last decades, giving rise to an impressive number of methods using different priors. Nevertheless, a thorough study of the latter, including especially sparse and tensor-based approaches, is still missing. Consequently, we proposed i) a taxonomy of the methods based on a priori assumptions, ii) a detailed description of representative algorithms, iii) a review of identifiability results and convergence properties of different techniques, and iv) a performance comparison of the selected methods on identical data sets. Our aim was to provide a reference study in the biomedical engineering domain which may also be of interest for other areas such as wireless communications, audio source localization, and image processing where ill-posed linear inverse problems are encountered and to identify promising directions for future research in this area. This work was published in the IEEE Signal Processing Magazine [14] .

**A sparsity-based approach**

Identifying the location and spatial extent of several highly correlated and simultaneously active brain sources from EEG recordings and extracting the corresponding brain signals is a challenging problem. In our comparison of source imaging techniques presented at ICASSP'14 [65] , the VB-SCCD algorithm [81] , which exploits the sparsity of the variational map of the sources, proved to be a promising approach. We proposed several ways to improve this method. In order to adjust the size of the estimated sources, we added a regularization term that imposes sparsity in the original source domain. Furthermore, we demonstrated the application of ADMM, which permitted to efficiently solve the optimization problem. Finally, we also considered the exploitation of the temporal structure of the data by employing L1,2-norm regularization. The performance of the resulting algorithm, called SISSY, was evaluated based on realistic simulations in comparison to VB-SCCD and several state-of-the-art techniques for extended source localization. This work was partially presented at EUSIPCO'14 [64] and a journal paper is in preparation.

**Tensor- and sparsity-based approaches**

The separation of EEG sources is a typical application of tensor decompositions in biomedical engineering. The objective of most approaches studied in the literature consists in providing separate spatial maps and time signatures for the identified sources. However, for some applications, a precise localization of each source is required.

To achieve this, a two-step approach was presented at the IEEE EMBC conference [26] . The idea of this approach is to separate the sources using the canonical polyadic decomposition in the first step and to employ the results of the tensor decomposition to estimate distributed sources in the second step, using the SISSY algorithm [64] .

Next, we proposed to combine the tensor decomposition and the source localization in a single step [27] . To this end, we directly imposed structural constraints, which are based on a priori information on the possible source locations, on the factor matrix of spatial characteristics. The resulting optimization problem was solved using the alternating direction method of multipliers (ADMM), which was incorporated in the alternating least squares tensor decomposition algorithm. Realistic simulations with epileptic EEG data confirmed that the proposed single-step source localization approach outperformed the previously developed two-step approach.

#### Independent component analysis

Participant : Laurent Albera.

Main collaboration: Sepideh Hajipour (LTSI & BiSIPL), Isabelle Merlet (LTSI, France), Mohammad Bagher Shamsollahi (BiSIPL, Iran)

Independent Component Analysis (ICA) is a very useful tool to process biomedical signals including EEG data.

We proposed a Jacobi-like Deflationary ICA algorithm, named JDICA. More particularly, while a projection-based deflation scheme inspired by Delfosse and Loubaton's ICA technique (DelL${}^{\mathbb{R}}$) [80] was used, a Jacobi-like optimization strategy was proposed in order to maximize a fourth order cumulant-based contrast built from whitened observations. Experimental results obtained from simulated epileptic data mixed with a real muscular activity and from the comparison in terms of performance and numerical complexity with the FastICA [90] , RobustICA [114] and DelL${}^{\mathbb{R}}$ algorithms, show that the proposed algorithm offers the best trade-off between performance and numerical complexity. This work was published in the IEEE Signal Processing Letters journal [21] .

In addition, we illustrated in the ICA context the interest of being able to solve efficiently the (non-orthogonal) JEVD problem. More particularly, we showed that, when the noise covariance matrix is unknown and the source kurtoses have different signs, the joint diagonalization problem involved in the ICAR method [58] becomes a non-orthogonal JEVD problem. Consequently, by using our JET-U algorithm [98] , giving birth to the MICAR-U (Modified ICAR based on JET-U) technique, we then provided a more robust ICA method. The identifiability of the MICAR-U technique was studied and proved under some conditions. Computer results given in the context of brain interfaces showed the better ability of the MICAR-U approach to denoise electro-cortical data compared to classical ICA techniques for low signal to noise ratio values. These results were presented in [98] .

#### Semi-nonnegative independent component analysis

Participant : Laurent Albera.

Main collaboration: Lu Wang (LTSI, France), Amar Kachenoura (LTSI, France), Lotfi Senhadji (LTSI, France), Huazhong Shu (LIST, China)

ICA plays also an important role in many other areas including speech and audio [62] , [63] , [74] , [71] , radiocommunications [79] and document restoration [111] to cite a few.

For instance in [111] , the authors use ICA to restore digital document images in order to improve the text legibility. Indeed, under the statistical independence assumption, authors succeed in separating foreground text and bleed-through/show-through in palimpsest images. Furthermore, authors in [82] use ICA to solve the ambiguity in X-ray images due to multi-object overlappings. They presented a novel object decomposition technique based on multi-energy plane radiographs. This technique selectively enhances an object that is characterized by a specific chemical composition ratio of basis materials while suppressing the other overlapping objects. Besides, in the context of classification of tissues and more particularly of brain tumors [107] , ICA is very effective. In fact, it allows for feature extraction from Magnetic Resonance Spectroscopy (MRS) signals, representing them as a linear combination of tissue spectra, which are as independent as possible [110] . Moreover, using the JADE algorithm [72] applied to a mixture of sound waves computed by means of the constant-Q transform (Fourier transform with log-frequency) of a temporal waveform broken up into a set of time segments, the authors of [71] describe trills as a set of note pairs described by their spectra and corresponding time envelopes. In this case, pitch and timing of each note present in the trill can be easily deduced.

All the aforementioned applications show the high efficiency of the ICA and its robustness to the presence of noise. Despite this high efficiency in resolving the proposed applicative problems, authors did not fully exploit properties enjoyed by the mixing matrix such as its nonnegativity. For instance in [82] , the thickness of each organ, which stands for the mixing coefficient, is real positive. Furthermore, reflectance indices in [111] for the background, the overwriting and the underwriting, which correspond to the mixing coefficients, are also nonnegative. Regarding tissue classification from MRS data, each observation is a linear combination of independent spectra with positive weights representing concentrations [87] ; the mixing matrix is again nonnegative.

By imposing the nonnegativity of the mixing matrix within the ICA process, we showed through computer results that the extraction quality can be improved. Exploiting the nonnegativity property of the mixing matrix during the ICA process gives rise to what we call semi-nonnegative ICA. More particularly, we performed the latter by computing a constrained joint CP decomposition of cumulant arrays of different orders [100] having the nonnegative mixing matrix as loading matrices. After merging the entries of the cumulant arrays in the same third order array, the reformulated problem follows the semi-symmetric semi-nonnegative CP model defined in section 7.4.1 . Hence we use the new method described in section 7.4.1 to perform semi-nonnegative ICA. Performance results in biomedical engineering were given in the paper cited in section 7.4.1 .