Model Breaking Detection using Independent Component Classi er Georges Linares, Pascal Nocera, Henri Meloni e-mail: georges.linares,pascal.nocera,
[email protected] C.E.R.I. 339 Chemin des Meinajaries BP 1228-84911 Avignon Cedex 9 France
Abstract. This paper presents a neural architecture for model breaking detection in real world conditions. This technique use an Independent Component Classi er [1] for detection of unexpected or unknown events in noisy and varying environment. This method is based on subspace classi er [2] and Independant Component Analysis [3]. A feed-forward neural network adapts itself to input evolutions, by detecting novelties, creating and deleting classes. A second process achieves a prototype rotation in order to minimise mutual information of di erent classes. This synaptic weight evolution rule is based on an anti-hebbian learning rule inspired from neural methods for blind separation of sources [4]. Consequently, under the assumption of statistical independence of di erent classes, the system is able to detect novelties hidden by simultaneous acoustic events. Novelty detection performances in various situations have been tested : isolated novelty, novelty which occurs mixed with an event of a known class, and several simultaneous novelties. We have also studied the evolution of detection performances obtained by varying the noise level. These experiments have shown good detection performances and low false detection rate.
1 Introduction Several methods for model breaking detection have been developped over the last few years. These methods were based on low level acoustic signal processing. Breaking of signal features regularity was the criterion used [5]. Such a predictability and regularity assumption may be right in highly constrained context. Unfortunately, real world acoustic environments are generally composed of several independant sources of acoustic events. Therefore, the acoustic signal is a mixture of several components, and disruptive acoustic events detection must be achieved by using higher level processing. On the other hand, real world environments are unpredictable, and modeling acoustic contexts by explicit knowledge based systems or by supervised learning models is dicult because of a large noise and context variability. In this paper, we propose a self-organized neural method for model breaking detection in real world acoustic environments.
2 Principle We use a neural classi er for modeling acoustic environment. Modeling is achieved by on-line clustering of the di erent acoustic components, using an ICC classi er. Such a network is able to dynamically learn and update a classi cation of input stimuli with minimum assumptions about event patterns and statistical acoustic ow features. A self-organization process allows to adapt the model to input evolutions. This adaptation is made on-line, by creating and deleting classes, and by prototypes evolution. We use the bad clustering of an acoustic event as a disruption criterion. Only the rst occurence of a recurrent event must be detected as disrupting, but the other occurences will not be considered as breaking.
3 Architecture ICC neural network has two fully inter-connected layers. The input layer receives the coecients of stimuli vectors. The output layer has one cell per class, and another for novelty. Each class is represented by a prototype Pit, an instantaneous activity and an inertia Ii(t). 3.1
Transfer function
Cell activities are computed by the projection of stimuli vectors into the prototype space. It is assumed that class prototypes are linearly independent. This assumption is implicitly respected in the new class acquisition stage. The output vector At is computed at time t by : At = P+t (t)Vt where V (t) is the stimulus vector, P the prototype matrix (each column of P is a prototype vector), and P + the pseudoinverse of P. Consequently, the weight matrix is the pseudoinverse of the prototype matrix. Wt = P+t A situation of simultaneous class activations is interpreted as the simultaneous presence of di erent event classes. 3.2
Novelty cell activation
The novelty cell activity is computed by: Vct k act0(t) = kVtk? Vt k where Vct is the component of Vt inside the prototype space.
Therefore, the input space is divided into two subspaces : the prototypes subspace, which is the subspace of known acoustic vectors, and its complementary subspace. When a stimulus is signi cantly into the unknown subspace, a model breaking is detected. The system adapts itself to this novelty detection by creating a new class which models the new cluster of the acoustic events occurred. 3.3
Inertia
The class inertia is computed from the temporal signal of the cell activity by a classical alpha-beta lter: Ii (t + dt) = kacti (t)k + (1 ? )Ii (t) The choice of parameter determines the persistence of the network's memory. This rule leads to delete low activity classes. Such low activation is due to low class representativness, or to the disappearance of a class of events.
4 Dynamic classi cation learning Initially, the system is empty, so there is no known class. A new class is created when novelty cell activation exceeds a xed vigilance threshold. In this case, the new class prototype is the input vector. The system permanently scans class inertias. If one of them is lower than a deletion threshold, then the class will be killed. This on-line class integration and deletion process induces a stabilisation of subspace dimension. Unfortunately, such an adaptation process is not sucient for good detection and classi cation of mixed acoustic events : if several disrupting events occur at the same time, then a new class is created with mixed spectral patterns as a prototype. Such a situation can leads to high correlation between class activities. Consequently, the system is reorganized in order to recover original events from their mixtures. The synaptic weight evolution rule is based on the minimisation of the statistical class dependence. This problem is similar to a blind source seperation problem. Therefore we use a neural source separation method inspired from [4] for prototypes reorganization. This method is able to learn a decorrelation operator Ct from linear mixtures of signals. We can easily show that the application of the decorrelation operator Ct to the classi er's outputs is equivalent to a prototype rotation:
S (t) = Ct:Wt+ V (t) = (WtCt?1)+ V (t)(1) where S (t) is the vector of uncorrelated outputs at time t, Wt the wheight matrix, V (t) the input pattern, and Ct the decorrelation operator. The last equation shows that we can use the uncorrelation operator learned with classi er outputs as a prototypes evolution rule. This process improves
modeling quality in real environments, and then improves the ability to detect novelties in complex situations, such as simultaneous novelties or novelty masked by a known class event.
5 Experiments In order to evaluate our system, we have mixed two sequences of recurrent transients and an underwater background noise. The rst is a rather low frequency transient, in the high-energy background noise domain. Therefore, it is more dicult to detect it (and also more dicult to recognize it in the spectrogram).Figures 1 to 5 shows on their rst line the signal spectrogram obtained by a FFT computed in a sliding temporal window. There is a vector of 256 coecients for each 10 ms. The second line shows the novelty cell activity. New classes are detected from the local maximums of that curve. The third line represents the prototype space dimension relatively to the input space dimension. The other lines shows most meaningful class activities. Detection performances have been evaluated in several scenarios : classes never simultaneous ( gure 1), new class apparition mixed with an event of an existing class ( gure 2), several simultaneous novelties ( gure 3). For these three situations, novelties were correctly detected, and there was no false detection. We have observed a good robustness when increasing the level of background noise ( gure 4 ). Nevertheless, these results are dependent on the frequency distribution of noise in relation to acoustic event features. A detection delay can be observed if the level of background noise is even more increased (5).
6 Conclusion and future prospects Our tests have validated the principle of the proposed system for novelty detection. Model breaking in cocktail situations has been correctly detected. Therefore, some diculties must be overcome in order to apply this method to the modeling of high temporal structure acoustic events. We are currently working on this problem.
References 1. G. Linares, P. Nocera, and H. Meloni. Mixed acoustic events classi cation using subspace classi er and ica. In Proc. 1997 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 1997. 2. T. Kohonen. Self organisation and Associative Memory. Springer Series in Information Sciences, third edition, 1989. 3. J. Karhunen and J. Joutsensalo. Representation and separation of signals using nonlinear pca type learning. Neural Networks, 7(1):113{127, 1994. 4. K. Matsuoka, M. Ohya, and M. Kawamoto. Neural net for blind separation of nonstationary signals. Neural Networks, 8(3):441{419, 1995. 5. W.Y. Liu, I. Magnin, and G. Gimenez. Operateur pour la detection de rupture dans des signaux bruites. Traitement du Signal, 12, 1995.
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