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Audio visual speech source separation via improved context dependent association model
EURASIP Journal on Advances in Signal Processing volume 2014, Article number: 47 (2014)
Abstract
In this paper, we exploit the nonlinear relation between a speech source and its associated lip video as a source of extra information to propose an improved audiovisual speech source separation (AVSS) algorithm. The audiovisual association is modeled using a neural associator which estimates the visual lip parameters from a temporal context of acoustic observation frames. We define an objective function based on mean square error (MSE) measure between estimated and target visual parameters.
This function is minimized for estimation of the demixing vector/filters to separate the relevant source from linear instantaneous or timedomain convolutive mixtures. We have also proposed a hybrid criterion which uses AV coherency together with kurtosis as a nonGaussianity measure. Experimental results are presented and compared in terms of visually relevant speech detection accuracy and output signaltointerference ratio (SIR) of source separation. The suggested audiovisual model significantly improves relevant speech classification accuracy compared to existing GMMbased model and the proposed AVSS algorithm improves the speech separation quality compared to reference ICA and AVSSbased methods.
1 Introduction
Audiovisual speech source separation (AVSS) is a growing field of research that is developed in recent years. It is derived from mixing audiovisual speech processing (AVSP) and blind source separation (BSS) techniques.
Speech is originally a bimodal audiovisual process. Perceptual studies on human audition have revealed that visual modality has effective contributions in speech intelligibility [1], perception [2] and detection [3] especially in the noisy and multisource (cocktail party) situations. According to the McGurkMcDonald effect [4] (that is, sensing the auditory part of a phonetic sound with visual part of another one, results in illusion of perception of a third one), it is evident that there is an early stage interaction between audio and visual stimuli in the brain. This is confirmed in [5] that early integration of audio and visual modalities can help in the identification and hence enhancement of speech in noisy environment. The performance of automatic speech processing systems degrades drastically in the presence of noise or other acoustic sources. Thus, researchers have tried to incorporate visual modality to automatic speech processing systems upon the perceptual findings.
Both audio and visual modalities of speech originate from gestures and dynamics of articulators along the speaker’s vocal tract. Hence, there is an intrinsic relation between these two speech cues. Although among all articulators, just the lip and, partially, jaws are visually observable. This partial observation bears a stochastic but exploitable relation between audio and visual cues.
It is inspiring to consider AV relation as two coherent and complementary components. In the automatic speech processing community, there has been early notification and interest (since 1984 [6]) for exploiting the complementary (orthogonal) portion of AV information prior to its coherent (nonorthogonal) portion. The complementary information of AV data is truly adopted in audiovisual speech recognition (AVSR) in either of early (feature), middle (model), or late (decoding) stage fusion schemes to enhance robustness against acoustic distortions. In recent years (since 2001 [7]), researchers have proposed methods based on exploiting the coherent component of AV processes for applicable tasks like speech enhancement [7–9], acoustic feature enhancement [10], visual voice activity detection (VVAD) [11], and AV source separation (AVSS) [11–24].
In [12], a statistical AV model based on Gaussian mixture models (GMMs) is presented for measuring the coherency of audio and its corresponding video and is used for extracting speech of interest from instantaneous squared mixtures on a simple French logatoms AV corpus. They have extended their method in [14] and assessed it on a more general sentence corpus and also for degenerate mixtures. Wang et al. [15] have exploited a similar GMM model (but using different AV features) as a penalty term for solving convolutive mixtures. That method seems to be inefficient because it should convert the separating system from frequency to time domain repeatedly. Rajaram et al. [13] have incorporated visual information in a Bayesian AVSS for separation of twochannel noisy mixtures. Their method adopts a Kalman filter with additional independence constraint between the states (sources). Rivet et al. [16] have adopted the AV coherency of speech (measured by a trained logRayleigh distribution) for resolving the permutation indeterminacy in the frequency domain separation of convolutive mixtures. They have also proposed another method [11] for convolutive AVSS based on developing a VVAD and using it in a geometric separation algorithm using sparse source assumption.
Sigg et al. in a pioneering work [17] have proposed a single microphone AVSS method by developing a nonnegative sparse canonical correlation analysis (NSCCA) algorithm. Their method jointly separates audio signals and localizes their corresponding visual sources. Following them, Casanovas and Monaci et al. [18–21] have proposed single microphone AV separation and localization methods by sparse and redundant atomic representation of AV signals. They use crossmodal correlations between AV atoms as similarity measure to cluster visual atoms for localizing visual sources and then separating audio signals.
Liang et al. [22] have incorporated visual localization to improve the fast independent vector analysis (FastIVA) as a frequency domain convolutive method. They use location of sources for smart initialization of FastIVA to solve its block permutation. Liu et al. [23] have proposed an AV dictionary learning method (AVDL) and have used it for AVBSS via bimodal sparse coding to estimate timefrequency (TF) masks.
Khan et al. [24] have proposed a videoaided separation method for twochannel reverberant recordings which estimates direction of sources via visual localization to be used in probabilistic models which are refined using EM algorithm and evaluated at discrete TF points to generate separating masks.
In this paper, we develop a visually informed speech source separation algorithm called MLPAVSS which considers temporal dependency between consecutive AV frames. We have suggested to model AV coherency using a multilayer perceptron (MLP) for AV association. This model with lower number of parameters can capture AV coherency significantly better relative to the GMM AV model of [12, 14, 15]. We have also proposed a hybrid measure of kurtosis and visual coherency and based on that a time domain convolutive AVSS algorithm. We have assessed quality of suggested AV model and its induced AVSS methods on two discrete (alphadigits) and continuous (poetverses) audiovisual corpora. The former is a corpus of Persian and English alpha digits and the later is a corpus of poem verses from about 20 Persian poets.
The rest of this paper is organized as follows: In Section 2, we briefly review BSS and AVSP background and then focus on the relevant AVSS work. Section 3 illustrates the proposed MLPbased AV model and AVSS algorithm. Section 3.3 presents a hybrid AV coherent and independent criterion, and based on that, we move toward a timedomain convolutive extension. In Section 4, audiovisual materials including AV corpus, parametrization and modeling procedures is considered. In Section 5, experimental setup and the experimental results are illustrated and analyzed. Finally, the paper is concluded in Section 6.
2 Background review
AVSS has emerged from mixing BSS and audiovisual speech processing techniques [16]. In this section, after a brief review of BSS and AV speech processing background, we explain the speech separation in terms of standard source separation problem and then discuss the suggested AV separation approach as an improved solution for this problem.
2.1 Blind source separation problem
Commonly, a blind source separation problem is briefly defined by its forward mixing model. In this paper, we consider the problem of source separation from a linear instantaneous mixture defined as
where $\mathbf{s}\left(t\right)=\phantom{\rule{0.3em}{0ex}}{\left[{s}_{1}\right(t),\dots ,{s}_{N}(t\left)\right]}^{T}\in {\mathbb{R}}^{N}$ is vector of source samples, $\mathbf{x}\left(t\right)={\left[{x}_{1}\right(t),\dots ,{x}_{M}(t\left)\right]}^{T}\in {\mathbb{R}}^{M}$ is vector of mixed signals and $\mathbf{A}\left(t\right)\in {\mathbb{R}}^{M\phantom{\rule{0.3em}{0ex}}\times \phantom{\rule{0.3em}{0ex}}N}$ is mixing matrix at the time instance t. It should be noticed that both s(t) and A are unknown. Hence, the problem is designated to BSS that is estimation of unmixed signals y(t)=B(t)x(t) from mixed signals x using an unknown demixing matrix B such that they are as similar as possible to unknown sources s.
In (1), both sources s(t) and the mixing process A(t) are considered nonstationary in time. In speech processing, sources (i.e. speech signals) are naturally nonstationary since (i) phonemes (and even subphonemes) have different waveform statistics and (ii) a speaker may either speak or be silent over the time. Also, the mixing model may be nonstationary in time because speakers may have motion relative to sensors (microphones). It is hard to solve the problem in this case; however, if sources and mixture can be considered piecewise stationary, a solution is to divide signals to batches and solve the BSS on each batch separately:
where τ is the batch index iterating over all batches of the signals. In this case, the mixing and demixing models (A_{ τ }, B_{ τ }) are time invariant during each batch. Another solution is to consider adaptive source separation techniques which is beyond the scope of this paper.
Independent component analysis (ICA) is the most wellknown family of solutions for BSS problems in which algorithms such as Infomax [25], FastICA [26] and JADE [27] (to name famous ones) try to estimate sources by adopting the statistical independence assumption. The solution of most ICA algorithms is based on optimizing their specific objective functions J(B;x) which measure the independence via different orders of signal’s statistics. Demixing matrix is then estimated by:
2.2 Audiovisual speech processing
Before explaining AV source separation methods, it is necessary to review some issues in AV speech processing which also inherently arises in AV source separation:

The speech signal and lip video are nonstationary in time.

The rate of speech samples and video frames is significantly different. In this study, the speech signal is recorded by F s=16,000 Hz while the video frame rate is F r=30 fps.

Speech signal and video frames have large numbers of samples (pixels) containing sparse information. This prevents creating audiovisual models directly from these signals.
To cope with first two issues, in most speech processing problems, speech is processed framewise with frames of 20−30 ms length where speech signal can be considered stationary. In AV speech processing, it is convenient to choose speech frame length such that audio and video frame rates are equal.
For handling the third issue, the routine solution is to extract compact and informative acoustic and visual features from speech and video frames such that each frame is represented with a few number of parameters. Framewise processing of speech is practical in most speech processing tasks, but the amount of speech signal in a single frame may be insufficient for source separation algorithms to perform accurately. Hence, a couple of consecutive frames must be used in each batch τ.
2.3 Audiovisual source separation
Consider problem of speech source separation in the case of instantaneous mixture of Equation (2). Most solutions (including ICAbased ones) have two major drawbacks which limit their applicability. The major problem is that ICAbased methods can estimate sources just up to a scale D and permutation P
that is signal’s amplitude gain and their order cannot be determined using these algorithms. The permutation of estimated sources may also change within consecutive frames, because sources are nonstationary in time and space. Having true or at least stable ordering of sources is crucial in most automatic speech processing applications. Furthermore, ICAbased methods do not consider or perform weakly in case of noisy and degenerate mixtures (i.e., mixtures with M<N).
Incorporation of visual modality of speech as a source of extra information, can help to solve these problems. The permutation problem can be simply resolved and enhancement in the separation performance is gained in regular and degenerate mixtures.
Most AVSS algorithms work based on maximization of AV coherency between unmixed signals y and their corresponding video streams. It is shown in [12] that given coarse spectral envelope of sources, one can solve a system of equations for calculation of demixing matrix in regular mixtures. Moreover there exists a stochastic coherent relation between the speech spectral envelope and the lip visual features [9, 12]. These two facts have guided researchers toward capturing AV relation using different models and adopt it for AVSS tasks.
In [12] and [14], authors have proposed a joint statistical distribution ${p}_{\text{av}}(\mathcal{S},\mathcal{V})$ as an AV model which measures the coherency between the acoustic spectral () and lip visual () features of the speech in each frame. The distribution ${p}_{\text{av}}(\mathcal{S},\mathcal{V})$ is modeled by the GMM and is trained using a corpus of corresponding AV streams via the Expectation Maximization (EM) algorithm.
Suppose that one of sources, say s^{1}, is a speech signal for which we have a video feature stream ${\mathcal{V}}^{1}$ extracted from the corresponding speaker’s lip region. Then, the AVSS algorithm of [12, 14] tries to estimate the first row of demixing matrix B^{1} for which the output y^{1}=B^{1}x produces spectral features ${\mathcal{Y}}^{1}$ as coherent as possible to the video features ${\mathcal{V}}^{1}$. This can be done by minimizing the AV incoherency score of the following AV model which is defined on each AV frame k:
However, due to the visemephoneme ambiguity problem [28, 29], it is possible that video features ${\mathcal{V}}^{1}$ in some frames be associated to many spectral configurations. Hence, the single frame criterion (5) will result in very poor separation. Consequently, they have proposed a batchwise AV criterion which integrates joint loglikelihood on all the T frames of current batch τ:
The summation in (6) is based on the assumption that AV frames in consecutive frames are independent from each other.
In the rest of this text unless mentioned otherwise, we always consider a single row demixing vector denoted by B corresponding to a single visual stream. For the sake of brevity we omit the superscript (.)^{1} of variables. It is clear that in case of existence of multiple video streams corresponding to more than one speech sources, all the described methods can be repeated for each video stream.
3 Audiovisual speech source separation using MLP AV modeling
Here, a method is proposed for separation of the source of interest s from M mixed signals x. The goal is to estimate B such that y=B x be similar as possible to the original source s. s is unknown but we have the visual stream corresponding to it, we can estimate B such that $\widehat{\mathcal{V}}$ (the estimated visual stream corresponding to y), be as close as possible to .
A problem with objective function (6) of [12] and [14] is that it does not efficiently model the nonlinear AV relation (as is discussed later in this section). Also it considers independence (i.i.d) assumption in modeling relation of consequent AV frames. We suggest to improve the AV criterion via more realistic assumptions.
Consider the batchwise separation problem of equation (2) where every batch τ consists of T frames. It is ideal to model and measure the degree of AV coherency on the joint whole sequences of audio ${\mathcal{S}}_{\tau}(1\phantom{\rule{0.3em}{0ex}}:\phantom{\rule{0.3em}{0ex}}T)$ and visual ${\mathcal{V}}_{\tau}(1\phantom{\rule{0.3em}{0ex}}:\phantom{\rule{0.3em}{0ex}}T)$ frames considering the true dependency among the variables. Let ${\mathcal{\mathcal{M}}}_{IDL}({\mathcal{S}}_{\tau},{\mathcal{V}}_{\tau})$ be such an ideal model which measures the degree of incoherency between AV streams. Then, the demixing vector B_{ τ } may be estimated by minimizing the ideal AV criterion ${J}_{avIDL}(B;{\mathbf{x}}_{\tau},{\mathcal{V}}_{\tau})={\mathcal{\mathcal{M}}}_{IDL}({\mathcal{Y}}_{\tau},{\mathcal{V}}_{\tau})$.
However, training such an ideal model is not practical due to the need for large amount of AV training data and also due to its train and optimization complexity. Hence, considering some relaxation assumptions which factorizes the model to a combination of some reusable factor(s) is inevitable. The independent and identically distributed (i.i.d) assumption considered in GMM model of (6) is not a fit assumption for modeling the speech AV streams. Thus we propose an enhanced model with a weaker independence assumption. Instead of considering absolute independence between AV frames, we consider a conditional independence assumption that is the coherency of an AV frame can be estimated independent of other frames given a context of a few (K) neighbor frames.
An extension of ${p}_{av}(\mathcal{S},\mathcal{V})$ to model joint probability density function (PDF) of K consecutive AV frames is not efficient. GMM and Gaussian distributions with full covariance matrices are not suitable for modeling large dimensional random vectors since the number of free parameters of these models is of order O(d^{2}) relative to the dimension d of input random vectors. Increasing the input dimension by concatenation of K AV frames will result in a very complex model with huge number of free parameters that are not used effectively.
We propose to use a MLP instead of GMM and mean square error (MSE) criterion instead of negative log probability (as incoherency measure) to provide an enhanced AV criterion. The number of free parameters of an MLP with narrow hidden layer(s) is of order O(d_{ i }+d_{ o }) relative to dimensions d_{ i } and d_{ o } of its input and output. Moreover, MLP makes efficient use of its free parameters in learning nonlinear AV relation, according to its hierarchical structure compared to shallow and wide structure of GMM. MLP, like GMM, is differentiable relative to its input. Hence, an objective function defined based on MLP can be optimized with fast convergence using derivative based algorithms.
3.1 MLP audio visual model
Having acoustic and visual streams of feature frames and extracted from pairs of corresponding AV signals s and V (see Section 4.1), a contextdependent AV associator can be trained using a suitable nonlinear function approximator: $\widehat{\mathcal{V}}\left(k\right)=h\left({\mathcal{S}}_{e}\right(k\left)\right)$, where $\mathcal{Se}\left(k\right)=E\left(\mathcal{S}\right(k\phantom{\rule{0.3em}{0ex}}\phantom{\rule{0.3em}{0ex}}K/2\phantom{\rule{0.3em}{0ex}}\phantom{\rule{0.3em}{0ex}}1:k\phantom{\rule{0.3em}{0ex}}+\phantom{\rule{0.3em}{0ex}}K/2\left)\right)$ is an embedded vector obtained from a context of K audio frames around frame k. An option for embedding E, is to stacks the center frame of the context and the firstorder temporal difference of other frames. In this paper, we adopt an MLP with K input audio frames, one hidden layer of N_{ H } neurons and a single visual frame as output, to approximate the AV mapping h(.). The MLPbased AV incoherency model ${\mathcal{\mathcal{M}}}_{MLP}$ is then defined as
3.2 Audio visual source separation algorithm
To compensate for phonemeviseme ambiguity, the MLP model must be used in a batchwise manner. Hence, as in (6), AV criterion is boosted by integrating incoherency scores of T frames in each batch τ:
Beside the difference in negative log probability and mean square error, another difference between AV objective functions (6) and (8) is the form of independence assumption in measuring the incoherency. The former considers absolute independence (i.e., i.i.d.) between the frames while the later assumes conditional independence.
For each batch τ of mixed signals and having a visual stream ${\mathcal{V}}_{\tau}$ corresponding to one of the speech sources, the goal of separation is to find the demixing vector B_{ τ }. As in (3), this can be achieved by minimizing AV contrast function:
This can be done via first or secondorder derivativebased optimization methods. For example, using the delta rule of gradient decent, we have
where η is the learning rate which either is set to a fixed small number or is adjusted using line search. The gradient of J_{av MLP} with respect to B (omitting constant parameters for brevity) is calculated as:
In the last summation, the first term is gradient of MLP AV model with respect to its input acoustic context ${\mathcal{Y}}_{e}\left(k\right)$ and the second term is gradient of acoustic features with respect to the demixing model B. Gradientbased algorithm iteratively minimizes the problem (9). Starting from an initial point B_{ τ }(0), at each iteration i, the gradient (11) is calculated, and using (10) or a quasiNewton method, the improved demixing vector B_{ τ }(i+1) is estimated. This continues until the change in the norm of B_{ τ } or J_{av MLP}(B_{ τ }) becomes smaller than a predefined threshold.
Since the AV contrast function is not convex, the optimization algorithm is prune to local minima. Thus, selection of a good initialization point B_{ τ }(0) is important. A simple option may be to start from random initial points multiple times. Most ICA algorithms (including FastICA [26] and JADE [27]) start from uncorrelated or white signals. Thus, another suggestion for initial point B_{ τ }(0) is to apply PCA on mixed signals x_{ τ } of the current batch τ and, among eigenvectors, select a vector W that produces a signal y=W x which is most coherent with the visual stream ${\mathcal{V}}_{\tau}$ and use it as the initial point B_{ τ }(0).
Both the proposed and existing AVSS algorithms do not suffer from the permutation ambiguity due to the informed nature of AV contrast functions. Nevertheless, the scale indeterminacy should be considered in design of AV contrast function and optimization method. AV model must be invariant regarding a constant gain to audio signal; that is, it must comply with the following constraint:
3.3 AVSS using AV coherency and independence criterion
Although the existing and the proposed AV coherencybased methods provide improvements in speech source separation, but these methods totally neglect the useful constraint of independence of the sources. The statistical independence criteria used by ICA methods has been successful in many BSS methods. In this section, we consider the benefit of using AV coherency and statistical independence together to gain more enhancement in speech source separation.
3.3.1 Videoselected independent component
Due to permutation indeterminacy (4), separated signals from ICA methods can not directly be used in real speech processing applications. Further, to calculate output signal to interference ratio (SIR) performance of ICA methods, it is required to know which of the demixed signals is related to the source of interest.
AV incoherency scores from AV models may be incorporated to introduce loosely coupled videoassisted ICA [14]. For that, in each batch of signals, sources are estimated by ICA method, and the source with minimum incoherency relative to the visual stream is selected as speech of interest. JADE [27] is one of the most successful ICA methods because of its accurate separation and its uniform performance (equivariance property). In this paper, we use JADE algorithm together with MLP audiovisual model (for relevant source selection) as the video assisted JADE (denoted by JADEAV).
3.3.2 Hybrid video coherent and independent component analysis
Contrary to the previous section where a sequential and loose combination of ICA and AV coherency model was considered, here we propose a parallel and tight combination using a hybrid criterion which benefits from normalized kurtosis as a statistical independence measure in conjunction with the AV coherency measure.
Kurtosis and negentropy are used in ICA methods such as FastICA [26] which work by maximizing the nonGaussianity. The first kurtosisbased BSS method was presented in [30] to separate sources via deflation. It starts by prewhitening the observed signals. Then the first source is estimated as y=B x^{′} from white observations x^{′} using a normalized demixing vector B. It is estimated by maximizing the kurtosis of y, defined as kurt(y)=E{y^{4}}−3(E{y^{2}})^{2} (for zeromean y) that is done via a gradientlike method. The kurtosis value is zero for Gaussian signals while it is positive or negative for signals with super or subGaussian distributions. If both super and subGaussian sources are expected to be extracted, then absolute or squared value of kurtosis must be maximized.
In [26], Hyvarinen et al. proposed a fast fixed point algorithm for solving the constrained optimization of the kurtosis and a family of other negentropybased criteria under the normalized constraint for B which resulted in the wellknown FastICA algorithm.
The reason for prewhitening and forcing normalized constraint on B is that the kurtosis is not scale invariant (i.e. kurt(α y)=α^{4}kurt(y)) and hence it depends both on energy and nongaussianity of the signal. In [31] and [32] normalized kurtosis defined as
is adopted on direct observations. The normalized kurtosis is scale invariant (i.e. kurt_{ n }(α y)=kurt_{ n }(y),∀α≠0). Hence, it eliminates the necessity for prewhitening and normalization constraint on the demixing vector B. To gain further improvement, we propose a hybrid criterion based on combination of the AV criterion (8) and the normalized kurtosis:
where λ is a positive regularization coefficient. Since speech signal is known to have superGaussian distribution [33, 34], the kurtosis term is added with negative sign such that it tends to be maximized during minimization of (14).
It must be noted that, in short time durations, the kurtosis score is not robust and does not provide significant improvement. Thus, (14) is developed to be used for convolutive case where quite large batches are considered. In fact, our tests revealed that for small batch sizes used in instantaneous mixtures, the performance of the AV method using kurtosis penalty does not improve compared to the pure AV method.
3.4 Toward a time domain AVSS for convolutive mixtures
Here, we consider convolutive mixtures defined by a MIMO system of M × N FIR filters A=[A_{ i j }]. The mixture system can be represented in the the Zdomain as
We are interested in estimation of a 1 × M row vector B(z) of demixing FIR filters which separates the source S^{1}(z)=B(z)X(z) that is as coherent as possible with the video stream V^{1}. In [31], a time domain algorithm based on maximizing (normalized) kurtosis is presented which deflates sources onebyone using noncausal twosided FIR filters. We consider it as our baseline audioonly convolutive method in our experiments. Following [35], we define an embedded matrix notation which transforms the convoltive mixture (15) to an equivalent instantaneous mixture. Let x^{′}(n) be an embedded column vector defined in each time step n as:
It contains M(2L+1) observation samples and using it the convolutive demixing process for separation of signal s^{1} can be expressed as y(n)=B x^{′}(n) where B is a row vector containing coefficients of M demixing FIR filters each one having 2L+1 taps. This is just an instantaneous mixture with M(2L+1) virtual (embedded) observations and can be solved using the kurtosisbased method of [31] or using our proposed criteria (14).
As a final note, it should be mentioned that the reference method of [31], can estimate demixing filters up to a scale and time delay. Thus, a crosscorrelation step is necessary to fix the possible delay of filters. For further details please refer to [31]. When dealing with convolutive mixtures, it is necessary to calculate the objective scores on longer segments of signals since there are larger number of parameters to estimate.
4 Audiovisual data and models
Audiovisual corpus and model are building material toward realization and evaluation of the proposed AVSS algorithm which is a datadriven method. In the following, we look at AV corpus creation and models training.
4.1 Audiovisual data
To evaluate the proposed algorithm, we have recorded a proper AV corpora which is comparable in (size and complexity) to the corpora used in former research. Unlike [11, 12], we have not used lip blue makeups in data recordings since we do not need lip segmentation for extraction of geometric features such as width and height. Instead, the pixel gray values of speaker’s mouth region are used to extract the visual parameters. We have recorded two different types of corpora. The first corpus consists of discrete Persian and English alphabet and digits with a vocabulary size of 78 words (32 + 10 Persian and 26 + 10 English alphadigits). The second corpus is continuous and consists of 140 verses of Persian poets. Both corpora are uttered by a male speaker. Each corpus is recorded two times. The first recording is used for training AV models and the second recording is used in evaluation phase.
In each recording, camera is focused on the speaker’s mouth and a video stream together with a mono audio stream is recorded. The raw video is captured in VGA size, truecolor format (RGB 24 bits/pixel 8 bits/color) and at the frame rate of F_{ r }≃30 f p s and audio is recorded using 16 bits/sample and at sampling frequency of F_{ s }=16,000 Hz. The final mouth region video used in the experiments of this paper, is stored in truecolor 160 × 120 resolution frames. Sample lip region images from audiovisual corpus are shown in Figure 1a.
4.2 Audio and video parameter extraction
As discussed before, speech signal and lip image frames are highdimensional data with sparse information related to our task. Thus, parametrizing audio and visual frames to compact vectors is necessary. Here, we clarify the methods for audio and visual feature extraction.
4.2.1 Audio parametrization
In most speech processing tasks, log spectral envelope (cepstral) features are utilized as effective features. We use PCA projected (whitened) log power spectral density for the speech frames parametrization. Let $\mathcal{Y}\left(k\right)=f\left(\phantom{\rule{0.3em}{0ex}}y\right(k\left)\right)=f\left(B\mathbf{x}\right(k\left)\right)$ be acoustic feature mapping function which extracts k_{ a } spectral envelope features from every frame k of the estimated signal y. In practice, audio features are extracted from the spectrum Y of the demixed signal y. Since for the separation algorithm we need to efficiently calculate and its derivative with respect to the demixing vector B, we define an alternate audio feature extractor function F(.) which efficiently extracts features from the frequency domain representation of y:
where $Y=F\left\{y\right\}$ and $\mathbf{X}=F\left\{\mathbf{x}\right\}$ are the fast Fourier transform (FFT) of y and x, respectively. In the righthand side (RHS) of (17), we have used the linear property of FFT that is, for every matrix B, $F\left\{\mathbf{B}\mathbf{x}\right\}=\mathbf{B}F\left\{\mathbf{x}\right\}$. Thus, we can precalculate X using FFT and then for any value of the demixing vector B the frequency domain demixed signal Y (and its derivative) can be efficiently obtained without FFT recalculation. As in [14], we have considered n=32 spectral coefficients in the range [0,5,000]H z as Y(k) for each frame. Power spectrum vector of each frame Y(k) is then defined as P S_{ Y }(k)=[Y(k)⊗Y^{∗}(k)]^{T}, where ⊗, (.)^{∗} and (.)^{T} are elementwise product, complex conjugation and transpose operators. Although the lip and the speech spectral envelope shapes are correlated, but there is not any meaningful relation between the lip shape and speech loudness (energy). Thus, it is important to normalize the energy of power spectrum in each frame resulting in power spectral density (PSD). The PSD coefficients are then converted to decibels (dB) using logarithm
Finally, whitening is applied to reduce the dimension of acoustic feature vectors to k_{ a } elements. The acoustic whitening matrix ${\mathbf{W}}_{a}\in {\mathbb{R}}^{n\times {k}_{a}}$ is computed from the train data using eigenvalue decomposition and is used to project train and test feature vectors to k_{ a }element compact spectral acoustic vectors. The overall acoustic feature extraction function F(Y) is defined as follows:
The Jacobian of F with respect to B is derived in the Appendix in Equations 22, 23 and 24. The derived formulas are efficient and do not need FFT recalculation for different values of B. It is also worth to mention that the mapping F is invariant regarding a scalar multiplication (i.e. F(α B;X(k))=F(B;X(k)),∀α≠0). A property that entails gain invariance property (12) in AV contrast functions (6) and (8).
4.2.2 Video parametrization
In previous works, such as [11, 12, 14], authors have used geometric lip parameters that need lip contour detection to estimate the width and height of interior lip contour. We extract holistic visual features from all pixels of the mouth region. This requires less computation and does not require contour fitting. Let function g(.) be visual feature mapping function which extracts k_{ v } visual features from any video frame. We assume that mouth region can be extracted from video using detection and tracking algorithms. There exists efficient parametric head tracking algorithms such as [36] which can be adopted for this task. The corpus used in this paper simply provides lip region in each frame. To extract k_{ v } visual features, the mouth region of each frame is shrunk to 32 × 24 pixels and then reshaped to 768 × 1 image vectors. Finally, a PCA transform is applied to extract visual features. The PCA matrix ${\mathbf{W}}_{v}\in {\mathbb{R}}^{768\times {k}_{v}}$ is computed from the train data and is used to project train and test mouth region images to k_{ v }element visual parameter vectors. Figure 1b represents top major eigenvectors (eigenlips) in order.
The overall visual feature extraction function is defined as normalized projected gray values of mouth region: $\mathcal{V}\left(k\right)=g\left(\mathbf{V}\right(k\left)\right)={\mathbf{\text{Q}}}_{v}^{T}{\mathbf{W}}_{v}^{T}\mathbf{V}\left(k\right)$, where Q_{ v } is the diagonal scaling matrix calculated from square root of corresponding eigenvalues.
To assess and understand the virtue of visual features, a simple yet insightful simulation is illustrated in Figure 1c. In PCA, the eigenvector with largest eigenvalue captures most of the variance of dataset. Most variance of lip images during speaking is along opening and closing of lips. Thus, it is expected that the principal eigenvector ${\mathbf{W}}_{v}^{1}$ will model this direction of variation. To check this, we calculated mean vector µ_{ v } of all video frames in poetverses train corpus and illustrated its variations along the principal eigenvector ${\mathbf{W}}_{v}^{1}$ with negative and positive integer multiplies of square root of corresponding eigenvalue σ_{1}. Results presented in Figure 1c show that this has resulted in synthesized opening and closing of lip images.
Figure 2a,c demonstrates two segments of discrete and continuous speech from alphadigits and poetverses corpora and Figure 2b,d shows the corresponding first visual feature (before normalization). In discrete or slow speech, first visual feature shows a quasiperiodic shape corresponding to the periodic lip opening and closing. In continuous or fast speech, lip opening and closing is partial and this makes it more complex.
4.3 Building audiovisual models
In addition to estimation of transforms W_{ a } and W_{ v }, that are part of the AV feature mapping functions f(.) and g(.), the train set of each corpus is used to learn the AV models. The training set consists of synchronous sequences of AV pairs $\left(\mathcal{S}\right(k),\mathcal{V}(k\left)\right)$ extracted from the raw AV data. For training models with K>1, first, the embedded acoustic stream ${\mathcal{S}}_{e}$ is formed by Kfold embedding of frames of acoustic stream . Instead of stacking the K frames of context, it is better to stack the center frame together with temporal difference of other frames. This reduces the redundancy in the embedded vector. Then, embedded pairs $\left({\mathcal{S}}_{e}\right(k),\mathcal{V}(k\left)\right)$ are used to train models. Both GMM and MLP models are trained with different context sizes for fair comparison. But as experimental results of Section 5.1 shows, GMM degrades with K>1.
For training GMM models, AV components of each pair are concatenated and considered as samples of joint PDF p_{av}(.,.). These samples are used for estimation of GMM parameters using maximum likelihood via expectation maximization (EM) algorithm [37]. GMM distributions with various configuration of parameters (k_{ a }, k_{ v }, N_{ M }, K) are trained. To assure good training, for each setting, GMM distribution is trained 20 times using EM with random initialization and the best model is selected based on a validation subset of training data. Regularization by adding a small positive number in range [10^{−10},10^{−2}] to diagonal elements of covariance matrices was adopted to hold positive definiteness where necessary (specially for models with larger random vector dimensions).
MLP AV models are also trained on AV pairs $\left({\mathcal{S}}_{e}\right(k),\mathcal{V}(k\left)\right)$ with ${\mathcal{S}}_{e}\left(k\right)$ as input and $\mathcal{V}\left(k\right)$ as output. MSE criterion between true and estimated outputs $\mathcal{V}\left(k\right)$ and $\widehat{\mathcal{V}}\left(k\right)$ is used as the performance measure in training. This is the same criterion as what is used in contrast function (8). Networks were trained using the LevenbergMarquardt algorithm [38] and via early stopping based on validation subset to avoid overfit. As for GMM, MLP models with various configuration of parameters (k_{ a }, k_{ v }, N_{ H }, K) are trained. To avoid local minima in training, each model is trained 20 times with random initialization and the best model is selected based on the validation subset.
5 Experiments and results
For evaluation of the proposed method, we have conducted four sets of experiments at different stages. First fitness of AV models in capturing AV coherency is evaluated with some initial experiments providing enough data for hyperparameter selection of models. Then, multiple source separation experiments on regular (N×N) and degenerate (M×N,M<N) cases are conducted to compare performance of proposed MLPbased AVSS method with GMMbased AVSS and JADEAV method (defined in Section 3.3.1). Finally, experiments on convolutive 2×2 mixtures with filters of different length are presented to compare performance of the audioonly and the proposed hybrid method.
5.1 Audiovisual models assessment and selection
In this experiment, we preevaluate fitness of AV models and explore the effect of different parameters on their performance. Both MLP and GMMbased AV models need training and have hyperparameters to be selected. We should choose proper dimensions k_{ a } and k_{ v } of acoustic and visual parameters, the embedding context size K, the number of hidden neurons N_{ H } of MLP and the number of Gaussian components N_{ M } of GMM models. Although validation scores of trained models can be used to select best GMM and MLP models, but selection of models based on their capability of discrimination between coherent and incoherent speech is more reasonable since models are aimed to be used for source separation. Furthermore, such an experiment provides insights in virtual potentials of coherencybased AVSS methods.
5.1.1 Audiovisual pure relevant source detection
In this experiment, we compare incoherency scores between a visual stream V^{1} and two pure audio signals: a coherent signal s^{1} and an irrelevant signal s^{2}. For each frame in the test set, the signal which produces minimum incoherency score is recognized to be coherent with V^{1}. Experiments are performed for both AV models ${\mathcal{\mathcal{M}}}_{GMM}(\mathcal{S},\mathcal{V})$ (5) and ${\mathcal{\mathcal{M}}}_{MLP}(\mathcal{S},\mathcal{V})$ (7). For each model, different values of hyperparameters k_{ a } ∈ {2,4,6,8,10,12}, k_{ v }∈{2,4,6,8}, N_{ M },N_{ H }∈{4,8,12,16,20,24,28}, K∈{1,2,3,4,5,6} are examined. Finally, the percent of all frames which signal s^{1} is truly selected is reported as classification accuracy for different values of batch size T.
In [14], authors have evaluated the classification rate just against a single irrelevant signal which is uttered by a different male speaker. Our initial experiments revealed that classification accuracy for different irrelevant signals is variable depending on the speaker, the speech content of signal and alignment of silent parts of coherent and incoherent signals. Thus, to provide classification rates with high confidence, we conducted multiple simulations by performing coherency classification on the relevant signal s^{1}y against six distinct speech signals for s^{2} and reported the average recognition rate as the performance of models.
Furthermore, it is possible that AV models, in addition to AV coherency of speech, capture some parts of AV identity of speaker. To check for this, we chose coherent and irrelevant speech signals both from the same speaker. As much as AV models have captured speaker identity, this provides a classification problem which is more confusing and complex for them relative to choosing irrelevant signals from different speaker(s).
Comprehensive classification rates are presented in Figure 3 for both GMM and MLP models and for different values of k_{ a }, k_{ v } and T as free parameters. In this figure, other parameters (N_{ H } of MLP, N_{ M } of GMM and K of both models) are marginalized by selecting the maximum accuracy among them.
Table 1 presents optimal values for hidden (marginalized) parameters (N_{ H }, N_{ M } and K) of Figure 3 for different k_{ a } and k_{ v } configurations in both GMM and MLP models.
The common trends in classification rates of Figure 3 reveals following points:

1.
Accuracy of both MLP and GMM models is enhanced by increasing the number of batch frames T, acoustic features k _{ a } and visual features k _{ v }.

2.
Among these factors, batch size T has the highest impact and this is followed by k _{ a }; finally, k _{ v } has the lowest impact.
Comparing the trends in Figure 3 for MLP and GMM models, also reveals interesting points:

1.
MLP model performs significantly better relative to GMM in various AV dimensions.

2.
In lower visual dimensions (k _{ v }=2), MLP outperforms with a 10−15% gap relative to GMM model. In this case, even performance of MLP with worst condition (batch size T=4) is 5−6% higher than GMM with best condition (batch size T=16).

3.
In higher visual dimensions (k _{ v }=6,8), the difference between GMM and MLP is somewhat reduced.

4.
The improvements by increasing number of features k _{ a } and k _{ v } is bounded. For k _{ v }>8, in MLP, k _{ v }>6 in GMM and k _{ a }>8 in both models, no more significant enhancement is achieved. The model complexity increases in O(K.k _{ a }+k _{ v }) for MLP and O((K.k _{ a }+k _{ v })^{2}) for GMM and in some point, this results in overcomplex models for the problem (considering the amount of available training data).

5.
Contrarily, improvements by increasing batch size (T) continues upward and may reach perfect accuracy for enough large T values. This is because the value of T does not change the model size while increasing it introduces more information for decision making. However, it is important to mention that for real AVSS tasks, we cannot increase T arbitrarily. This makes the stationary assumption considered in the mixture model (2) invalid. Hence, there is a tradeoff on the value of T between the AV model accuracy and the mixing model fitness.
Finally, results of optimal K, N_{ H } and N_{ M } values presented in Table 1 reveals that

1.
In various k _{ a } and k _{ v }s, GMM always has performed better with K=1 frames in embedded context which means GMM can not capture temporal dynamics by frame embedding due to quadratic order of parameters.

2.
MLP always has performed better with K=2 frames (for greater k _{ a }) or K=4,6 frames (for smaller k _{ a }) in embedded context showing that it can capture some temporal dynamics.

3.
Both GMM and MLP models exploit maximum average number of latent units in k _{ v }=6 which seems to be efficient optimal visual dimension size according to results of Figure 3.
5.1.2 Audiovisual mixed relevant source detection
Recall that classification results in Section 5.1.1 are based on comparing the incoherency scores between pure relevant and irrelevant speech signals. This entails that AV models are well suited for selection of a clean relevant source among multiple available irrelevant signals. For example, it will perform well for relevant source selection in AVassisted ICAbased source separation method (i.e. JADEAV) discussed in Section 3.3.1.
In AV separation algorithms, models must provide scores for signal of relevant source which is more or less contaminated by other sources specially during first iterations of the optimization algorithm. Hence, a good AV model must be such that it provides decreasing incoherency scores for increasing amounts of SIR. Therefore, we conducted another experiment to assess how well AV models comply with this property. Let ξ_{ i }=s^{1}+α_{ i }s^{2} be a mixed signal composed of source s^{1} coherent with visual stream V^{1} and an irrelevant speech or acoustic signal s^{2}. Mixed signals at different SIRs can be generated using different values for mixing coefficient α_{ i }. We generated a set of mixed signals ξ_{ i } with SIRs in range [ −5,30] dB and performed classification using incoherency score comparisons between signal pairs (ξ_{ i },ξ_{i+1}) at different SIR levels. Here, the classification accuracy is defined as percent of all frames which the signal with higher SIR is selected. Figure 4 shows average classification accuracy measured at different SIR levels for all GMM and MLP models tested in previous experiment.
Generally, trends of Figure 4 shows similar properties as was discussed for Figure 3. The major point is that classification accuracies of best models on mixed signals ξ_{ i }, ξ_{i+1} is something about 10% less relative to classification of pure relevant and irrelevant signals. Such a degradation is predictable since signals ξ_{ i } and ξ_{i+1} are very similar. But the interesting note is that superior models in the pure classification have approximately kept their superiority in the mixed case. This means that optimal model configurations which are better for classification task, may keep their position in separation task. As before MLP models are superior to GMM models but the large gap between them is somewhat reduced.
5.2 Source separation experiments
5.2.1 Separation performance criterion
In our experiments, we will simulate the mixing process using some mixing matrices. Thus, we have original source signals and it is possible to calculate the SIR of each acoustic source specially the source of interest (s^{1}) in all mixed and demixed signals. Let e(s^{i}) be the energy of source i and x^{i} be the i^{th} mixed signal produced by the mixing matrix A. Then SIR of s^{1} in each of inputmixed observations can be calculated as
where a_{ i j } is the element of mixing matrix at positions i,j. The input SIR is useful for analysis of complexity of mixing matrices utilized in simulations. Similarly, consider B as estimated demixing vector for source s^{1} and let G=B A be the global mixing and demixing vector for this source. Then output SIR of s^{1} in estimated demixed signal y can be calculated as:
The output SIR criterion is widely used in performance evaluation of source separation algorithms when original source signals or mixing systems are available [39]. Since in our experiments, we perform batchwise separation, the output SIR is averaged over all batches in the test set. It is worth to mention that in convolutive mixtures, the SIRs must be calculated up to an allowed arbitrary filtering of the sources. This can be accomplished, using the decomposition method of Vincent et al. [39].
5.2.2 Separation in regular N×N mixtures
In this experiment, we consider regular N×N mixtures with equal number of sources and sensors. Simulations are performed for mixtures of different sizes N=2,3,5 and separation performance in terms of output SIR (21) is presented. Experiments are conducted on the test set of both alphadigits (Persian and English) and poetverses (Persian) corpora (see Section 4.1 for corpus details). Each corpus consists of a pair of synchronous audio and visual streams of frames. From each corpus, 3,000 frames are exploited in separation simulations. The audio stream from test corpus is considered as the relevant source s^{1} and for other N−1 sources, speech signals of the same length are used. These speech signals are selected from a supplementary corpus recorded from other speakers with the same sampling frequency.
Since the performance of GMM and MLPbased AVSS methods is not uniform in different mixture matrices, we have conducted MonteCarlo (MC) simulations with 20 different random mixing matrices for each mixture size N×N. Table 2 summarizes the average mixed input SIR of each sensor with respect to s^{1} in various mixtures. Input SIRs are also useful in analysing the gained SIR specially in degenerate mixtures where output results is very sensitive to chosen mixing matrices. Mixing matrices are kept the same for both corpora. For each corpus and mixture size, input mean SIRs are obtained by calculating average on all the simulated random mixing matrices.
For each corpus and mixture size, speech source separation using, JADEAV, GMMAVSS and MLPAVSS methods are conducted to all simulation matrices in order to estimate the demixing vectors. Then, the average output SIRs is calculated over all estimated demixing vectors and all batches of separated signals. Results are presented in Tables 3 (for N=2), 4 (for N=3) and 5 (for N=5). Since in this experiment, mixing matrices are squared and invertible, relatively highoutput SIRs are achieved in all tested configurations. Analysis and comparison of results in terms of separation algorithms, batch integration size, mixture size and corpus reveals the following points:
Effect of discrete and continuous speech: The performance of all methods is higher on alphadigits corpus compared to poetverses. Alpha digits corpus is discrete and poetverses corpus is continuous. It is obvious that continuous speech is more complex for AV modeling since lip formations are not well expressed due to speech speed (coarticulation) and also since in continuous corpus there is much number of different words and phonetic contexts which increases the phonetic complexity.
Relative separation performance of methods: In lower mixture sizes (N=2,3), MLPAVSS method provides higher output SIRs relative to GMMAVSS and both of them are superior to JADEAV for alphadigits corpus. In N=3,5 and for poetverses corpus, the performance enhancement gap between AVSS methods and JADEAV is reduced. In this case, performance gain of GMMAVSS is marginal and some times worst relative to JADEAV. The superiority of MLPAVSS relative to GMMAVSS is consistent with classification accuracies of MLP and GMMbased AV models presented in Section 5.1.
Effect of batch integration time (T): The performance of all methods increases with increasing the number of frames in each batch. Increasing the integration time enhances accuracy of contrast functions (6) and (8) (see Section 5.1) and also reduces spurious local minima in the optimization landscape. For JADEAV algorithm, in addition to improved accuracy of AV contrast, increasing integration time allows better estimates of higher order statistics of signals which affect separation quality of JADE algorithm. But recall that in real applications with nonstationary mixtures, there is a tradeoff for increasing number of frames in each batch (see Section 5.1).
5.2.3 Separation in degenerate M×N,M<N mixtures
In this experiment, we performed Monte Carlo simulations with 20 random matrices of size M×N=2×3. Average mixed input SIRs of two channels on all simulated random matrices and for all test frames of each corpus is presented in the corresponding columns of Table 2. Like before, demixing matrices are estimated by running the proposed and baseline source separation methods. Results are presented in Table 6.
In this case, the mixing matrices are degenerate and have not exact inverse. Hence, the perfect recovery of sources is not possible and SIRs are worse relative to regular N×N simulations. AVSS methods show slight improvements relative to JADEAV methods. The performance of MLPAVSS is again superior to GMMAVSS as is predicted. In this experiment, results were highly dependent on mixing matrix. In some mixtures, output SIRs near to 10 dB were achieved while in some others negative output SIRs were observed.
5.2.4 Separation of convolutive 2×2 mixtures
In this experiment, we considered separation of 2×2 convolutive mixtures using methods described in Section 3.4. We generated random mixing systems for each filter size (2L+1) and simulated the mixtures. The separation was conducted with the same number of (2L+1) taps for each demixing filter. Due to the complexity of the convolutive problem, it is necessary to use large batch sizes. So we considered batches of 5 s. Results in terms of output SIR are presented in Table 7.
For L=0, the mixture is instantaneous and separation is possible with high SIR. But for L=2 (filters with five taps) and for higher degree of mixing and demixing filters, the SIR decreases to about average 9 dB for audioonly method of [31] and 11 dB for the hybrid AV coherent and independent method proposed in Section 3.4.
6 Conclusion
In this paper, we proposed an improved AV association model using an MLP which exploits the dependency between AV frames and is superior to the existing GMM AV model. The MLP model makes efficient use of its parameters relative to the GMM model. Hence, unlike the GMM model, it can capture temporal dynamics from a limited context of frames around the current frame to enhance the coherency measure. We also proposed a hybrid criterion which exploits AV coherency together with normalized kurtosis as an independence measure and, based on that, moved toward a timedomain convolutive AVSS method. Experimental results for comparison of the methods are presented in terms of the relevant signal classification accuracy and also the separation output SIRs. Results, confirms the contribution of the proposed neuralbased AV association model in enhancement of AV incoherency scores and hence in improvement of the separation SIRs compared to the existing GMMbased AVSS algorithm and the visually assisted ICA (JADEAV) method. Also, results of the timedomain convolutive method, using hybrid AV criterion shows improvement compared to the reference audioonly method.
For visual parametrization part, we have used normalized PCAprojected (whitened) lip appearance features. PCA features do not need exact lip contour detection and hence require less computation compared to extraction of lip geometric (width and height) parameters. But it also has the drawback of being more sensitive to the speaker and segmentation of the lip region. The fitness of PCA features for AV modeling and AVSS task is justified by qualitative illustrations and numerical results. However, the proposed AVSS method is not coupled to the PCA visual features and it can be adopted with more robust and accurate visual features.
Although proposed model improves quality of AV modeling, but further enhancements is both required and predictable to make these methods applicable in more complex phonetic contexts and speakerindependent situations. AV relation is both nonlinear and stochastic. GMM benefits from its capability in probabilistic modeling. But GMM fails to efficiently handle the nonlinearity and temporal dependency. On the other hand, MLP seems to benefit from its relatively deep structure and efficient use of its parameters, but it does not truly consider stochastic property of AV relation. Further improvements may be gained by introducing a model which can efficiently handle both the nonlinear and the stochastic relations of the two modalities as well as the temporal dependency. Also, it seems promising to consider more essential combinations of ICA and AV coherencybased methods to jointly gain benefits of both informed and blind methods.
Finally, it is worth to mention that in this paper, we did not consider the interbatch temporal dynamics of demixing vectors and separated signals. It is possible to adopt this temporal information for example using a Bayesian recursive filtering approach to improve the performance and speed of proposed methods. Also, it is possible to adaptively determine the working batch size based on the amount of interbatch variations of the demixing vectors.
Appendix
Here, we derive the gradient $\frac{\partial {\mathcal{Y}}_{e}\left(k\right)}{\mathrm{\partial B}}$ which is required in calculation of (11). The embedded acoustic vector ${\mathcal{Y}}_{e}\left(k\right)$ is composed from individual acoustic frames $\mathcal{Y}\left(k\right)$. So we first need to calculate the gradient $\frac{\partial \mathcal{Y}\left(k\right)}{\mathrm{\partial B}}$ which is a Jacobian matrix of the size k_{ a }×M. Starting from (17) and (19), we have
where ${\left({\mathbf{W}}_{a}^{T}\right)}_{{k}_{a}\times n}$ is the PCA (whitening) transform, (PSD_{B X(k)})_{n×1} is the power spectral density vector of frame k, ${\left(\frac{\partial {\text{PSD}}_{B\mathbf{X}\left(k\right)}}{\mathrm{\partial B}}\right)}_{n\times M}$ is Jacobian of PSD vector with respect to B and ⊘ is the elementwise operator which divides each column of the left matrix by the right vector. The Jacobian of PSD is also calculated as:
where (PS_{B X(k)})_{n×1} is the power spectrum vector, ${\left({\Sigma}_{i}{\text{PS}}_{B\mathbf{X}\left(k\right)}^{i}\right)}_{1\times 1}$ is sum of its elements, ${\left(\frac{\partial {\text{PS}}_{B\mathbf{X}\left(k\right)}}{\mathrm{\partial B}}\right)}_{n\times M}$ is Jacobian matrix of the PS vector with respect to B and ${\left({\Sigma}_{i}\frac{\partial {\text{PS}}_{B\mathbf{X}\left(k\right)}^{i}}{\mathrm{\partial B}}\right)}_{1\times M}$ is the sum of rows of the Jacobian matrix. Finally, the Jacobian of PS is calculated as
A where Re{.} and Im{.} are real and imaginary part operators. Equations 22, 23 and 24 are derived for the calculation of Jacobian of a single frame. In our MATLAB implementation, we have derived more complex matrix forms which allows calculation of Jacobian of multiple acoustic frames (i.e. all frames in a batch) using efficient vectorized computing. Having Jacobian of individual acoustic frames $\mathcal{Y}\left(k\right)$, we combine the theme to obtain the Jacobian of embedded acoustic vectors ${\mathcal{Y}}_{e}\left(k\right)$. This is done according to the definition of the embedding method E.
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Kazemi, A., Boostani, R. & Sobhanmanesh, F. Audio visual speech source separation via improved context dependent association model. EURASIP J. Adv. Signal Process. 2014, 47 (2014) doi:10.1186/16876180201447
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Keywords
 Audiovisual speech source separation
 Bimodal coherency
 Blind source separation
 Independent component analysis