TECHNICAL FIELD

Embodiments presented herein relate to a method, a classification device, a computer program, and a computer program product for non-parametric audio classification.

BACKGROUND

In general terms, audio mining is a technique by which the content of an audio signal (comprising an audio waveform) can be automatically analyzed and searched. It is commonly used in the field of automatic speech

recognition, where the analysis tries to identify any speech within the audio. The audio signal will typically be processed by a speech recognition system in order to identify word or phoneme units that are likely to occur in the spoken content. In turn, this information can be used to identify a language used in the audio signal, which speaker that is producing the audio waveform, the gender of the speaker producing the audio waveform, etc. This information may either be used immediately in pre-defined searches for keywords, languages, speakers, gender (a real-time word spotting system), or the output of the speech recognizer may be stored in an index file. One or more audio mining index files can then be loaded at a later date in order to run searches for any of the above parameters (keywords, languages, speakers, gender, etc.).

The parameters can be represented by classes. That is, assuming that the audio signal is to be classified in terms of language, there may be a set of classes where each class represents a unique language, and where the classification intends determine which one of these languages is used in the audio signal.

Generally, a probabilistic based technique attempt to discover the thus unknown class through estimate a probability density function. In general terms, there are two major classes of such techniques, namely parametric (also known as model-based) techniques and non-parametric technique. Generally, parametric techniques assume a known form of the underlying probability density function and adjust the model parameters to available training data. This technique requires low computational and storage requirements, and it could be applied with limited amount of training data. One disadvantage is that the form of underlying probability density function is not known in most practical application, and therefore, a mismatch between the assumed form of the probability density function and the true form of the probability density function might occur.

Generally, non-parametric techniques have no prior assumption of the form of underlying probability density function, and therefore do not suffer from the mentioned above model mismatch. For example, a /c-Nearest-Neighbor approach attempts to estimate posterior probabilities Ρ(ω _πι for an unlabeled observation point x from a set of pre-stored L labeled training samples in the following way. In a first step, a cell is centered around x and grown until the cell captures k nearest neighbors of x. For example, k could be selected as VL. In a second step, if k _m of these samples are labeled ¾, then posterior probabilities are estimated as: k

Ρ(ύ½ |χ) = - m = l, - , M (1)

For a reliable estimate k has to be large and all k neighbors have to be close to x. This could be achieved by pre-storing large amounts of labeled training data, i.e., L→∞. Hence, non-parametric techniques come with

computational complexity and storage requirements, which are prohibited for many practical applications.

Hence, there is still a need for an improved classification of audio. SUMMARY

An object of embodiments herein is to provide efficient audio classification.

According to a first aspect there is presented a method for non-parametric audio classification. The method is performed by a classification device. The method comprises obtaining a short-term frequency representation of an audio waveform, the short-term frequency representation defining an input sequence divided into input vectors. The method comprises determining per- class posterior probabilities for at least two classes. Each per-class posterior probability is based on a weighted sum of pre-stored per-cluster posterior probabilities for the at least two classes. Each class represents a unique audio classification property. The method comprises classifying the input sequence to belong to the class for which the per-class posterior probability is largest.

Advantageously this provides efficient audio classification.

Advantageously this method is flexible with respect to the probability density function of the short-term frequency representation of an audio waveform by using a non-parametric estimation technique whilst only requiring low complexity requirements comparable to the complexity requirements of parametric based estimation techniques.

According to a second aspect there is presented a classification device for non-parametric audio classification. The classification device comprises processing circuitry. The processing circuitry is configured to cause the classification device to obtain a short-term frequency representation of an audio waveform, the short-term frequency representation defining an input sequence divided into input vectors. The processing circuitry is configured to cause the classification device to determine per-class posterior probabilities for at least two classes. Each per-class posterior probability is based on a weighted sum of pre-stored per-cluster posterior probabilities for the at least two classes. Each class represents a unique audio classification property. The processing circuitry is configured to cause the classification device to classify the input sequence to belong to the class for which the per-class posterior probability is largest.

Advantageously the proposed classification device requires low

computational effort for performing the non-parametric audio classification and is therefore practically implementable. According to a third aspect there is presented a classification device for non- parametric audio classification. The classification device comprises processing circuitry and a computer program product. The computer program product stores instructions that, when executed by the processing circuitry, causes the classification device to perform steps, or operations. The steps, or operations, cause the classification device to obtain a short-term frequency representation of an audio waveform, the short-term frequency representation defining an input sequence divided into input vectors. The steps, or operations, cause the classification device to determine per-class posterior probabilities for at least two classes. Each per-class posterior probability is based on a weighted sum of pre-stored per-cluster posterior probabilities for the at least two classes. Each class represents a unique audio classification property. The steps, or operations, cause the classification device to classify the input sequence to belong to the class for which the per- class posterior probability is largest.

According to a fourth aspect there is presented a classification device for non- parametric audio classification. The classification device comprises an obtain module configured to obtain a short-term frequency representation of an audio waveform, the short-term frequency representation defining an input sequence divided into input vectors. The classification device comprises a determine module configured to determine per-class posterior probabilities for at least two classes. Each per-class posterior probability is based on a weighted sum of pre-stored per-cluster posterior probabilities for the at least two classes. Each class represents a unique audio classification property. The classification device comprises a classify module configured to classify the input sequence to belong to the class for which the per-class posterior probability is largest.

According to a fifth aspect there is presented a computer program for non- parametric audio classification, the computer program comprising computer program code which, when run on classification device, causes classification device to perform a method according to the first aspect. According to a sixth aspect there is presented a computer program product comprising a computer program according to the fifth aspect and a computer readable storage medium on which the computer program is stored.

It is to be noted that any feature of the first, second, third, fourth, fifth and sixth aspects may be applied to any other aspect, wherever appropriate.

Likewise, any advantage of the first aspect may equally apply to the second, third, fourth, fifth, and/or sixth aspect, respectively, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following detailed disclosure, from the attached dependent claims as well as from the drawings.

Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the element, apparatus, component, means, step, etc." are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.

BRIEF DESCRIPTION OF THE DRAWINGS

The inventive concept is now described, by way of example, with reference to the accompanying drawings, in which:

Fig. l is a schematic diagram illustrating probability density functions of two classes;

Fig. 2 is a schematic block diagram of a classification device according to an embodiment;

Fig. 3 is a schematic diagram showing functional units of a classification device according to an embodiment;

Fig. 4 is a schematic diagram showing functional modules of a classification device according to an embodiment; Figs. 5 and 6 are flowcharts of methods according to embodiments; and

Fig. 7 shows one example of a computer program product comprising computer readable storage medium according to an embodiment.

DETAILED DESCRIPTION

The inventive concept will now be described more fully hereinafter with reference to the accompanying drawings, in which certain embodiments of the inventive concept are shown. This inventive concept may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete, and will fully convey the scope of the inventive concept to those skilled in the art. Like numbers refer to like elements throughout the description. Any step or feature illustrated by dashed lines should be regarded as optional.

The embodiments disclosed herein relate to non-parametric audio

classification. In order to obtain non-parametric audio classification there is provided a classification device, a method performed by the classification device, a computer program product comprising code, for example in the form of a computer program, that when run on a classification device, causes the classification device 200 to perform the method.

As an introductory example, reference is now made to Fig. 1. Fig. 1 at 100 and 110 illustrate probability density functions of two classes ω ₁ and ω ₂ . Assume that a data point x _n = 4 (as identified by reference numeral 130 in Fig. 1) is observed. The task of the classification device is to assign the data point x _n to one of the classes ω ₁ and ω ₂ , without having any assumption of the underlying form of the probability density functions 100, 110. Further explanation of Fig. 1 will be provided below.

Reference is now made to Fig. 2. Fig. 2 is a schematic block diagram of a classification device 200 according to an embodiment. According to the embodiment of Fig. 2, the classification device 200 comprises a classification module 210 and an optional training module 220. In embodiments where the training module 220 is not present it is assumed that the classification module 210 is provided with values determined by an external training module 220

Figs. 5 and 6 are flow chart illustrating embodiments of methods for non- parametric audio classification. The methods are performed by the

classification device 200. The methods are advantageously provided as computer programs 320.

Reference is now made to Fig. 5 illustrating a method for non-parametric audio classification as performed by the classification device 200 according to an embodiment.

Step S102: The classification device 200 obtains a short-term frequency representation of an audio waveform. The short-term frequency

representation define an input sequence x which is assumed to be divided into input vectors x _n . More particularly, the input sequence x can be assumed to consist of n, N-dimensional, vectors and hence be written as x = {x _n }^ ₌₁ .

Step S104: The classification device 200 determines per-class posterior probabilities Ρ(ω for at least two classes ¾. Each per-class posterior probability Ρ(ω _πι based on a weighted sum Ρ(ω _πι of pre-stored per- cluster posterior probabilities Ρ(ω _πι \μ _1{ ) for the at least two classes ¾. Each class ω _τη (in the set of classes {%}* ₌₁ ) represents a unique audio

classification property- Step S106: The classification device 200 classifies the input sequence x to belong to the class ω _τη for which the per-class posterior probability Ρ(ω _πι is largest.

Embodiments relating to further details of non-parametric audio

classification as performed by the classification device 200 will now be disclosed. Reference is made to Fig. 6 illustrating methods for non- parametric audio classification as performed by the classification device 200 according to further embodiments. Steps S102, S104, S106 are performed as in Fig. 5 and a repeated description of those steps is therefore omitted.

According to an embodiment the weighted sum Ρ(ω _πι is determined using cluster contribution weights _k . The cluster contribution weights A _k are defined by distances A _k between the input sequence x and a set of clusters c _k . Each cluster c _k belongs to one of at least two classes ¾. In general there are M classes, and this is formally denoted by a set of classes {w _m }" ₌₁ . For a given class ω _τη the weighted sum Ρ(ω _πι when summed over all input vectors x _n , define the per-class posterior probability Ρ(ω _πι for this given class ω _τη .

There could be different properties that the at least two classes ω _τη represent. For example, the non-parametric audio classification can be performed to classify languages, speaker, and/or genders. Hence, according to an embodiment each class ω _τη represents a unique language, a unique speaker, or a unique gender.

There could be different ways for the classification device 200 to obtain the short-term frequency representation of the multimedia signal as in step S102. According to an embodiment the short-term frequency representation is provided by mel-frequency cepstral components (MFCCs). In this respect, the MFCCs are coefficients that collectively make up a mel-frequency cepstrum (MFC). The MFC is a representation of the short-term power spectrum of the audio waveform, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. There could be different ways for the classification device 200 to obtain the MFCCs. According to one embodiment the MFCCs are made readily available to the classification device 200. Hence, this embodiment requires a module configured to provide the MFCCs from the audio waveform. According to another embodiment the classification device 200 receives the audio waveform and extracts the MFCCs from the audio waveform. Hence, according to an embodiment the classification device 200 is configured to perform step Si02a: Step Si02a: The classification device 200 extracts the MFCCs from the audio waveform. How to extracts MFCCs from an audio waveform is as such known in the art and further description thereof is therefore omitted. Step Si02a is performed as part of step S102. Each input vector x _n can then correspond to a vector of MFCCs. Assuming that the audio waveform is composed of frames, there is then one vector of MFCCs per frame.

There could be different types of audio waveforms. According to an

embodiment the audio waveform represents a speech signal.

There could be different types of audio classification of which the herein disclosed methods for non-parametric audio classification could be part of. According to an embodiment the step S102 of obtaining, the step S104 of determining, and the step S106 of classifying are performed in an audio mining application.

According to an embodiment the methods for non-parametric audio classification comprise a training stage and a classification stage.

Aspects of the training stage, as implemented by the training module 220, will now be disclosed in detail.

According to an embodiment the per-cluster posterior probabilities Ρ(ω _πι \μ _1{ ) are determined through training on a training sequence y of MFCCs.

The training could be based on /c-means clustering of the training sequence y. Further details thereof will now be disclosed. Let the training sequence y consists of I, L-dimensional, vectors y = where each vector y is linked to its corresponding class ¾. First the training sequence y is by the classification device 200 organized in K clusters {c _fc }£ ₌₁ , by means of a k- means clustering algorithm. This results in a codebook with K, D dimensional centroids μ . In addition to its centroid, each cluster c _k is determined by its squared median absolute deviation factor p _k , which the classification device 200 determines as: p _k = median(|Z _fe — median(Z _fe ) |) ² (2)

Here, Z _k denotes the set of points that belong to cluster c _k (that is, Z _k is a sub-set of y in which every point is closer to μ than to any other mean μ _≠¾ ). In general, the median absolute deviation factor p _k is a robust to outliers statistic that captures variations inside cluster c _k . Hence, according to an embodiment where each cluster c _k has a cluster centroid μ , each per-cluster posterior probability Ρ(ω _πι \μ _1{ ) for a particular class ω _τη and a particular cluster centroid μ represents a conditional probability of the particular class ω _τη given the particular cluster centroid μ .

After the set of clusters c _k ≡ {μ , p _k }, for k = Ι, .,. , Κ is determined, the classification device 200 links each of the clusters c _k to a table of posterior probabilities for each of the M classes P (w _m \μ ), m = 1, ... , M. In order to do so the classification device 200 implements the following operations: for k = 1, ... , K determine the size of Z _k and store it in a variable L _k ; for m = 1, ... , M: count the number of labels of class ω _τη and denote it as L^ ^m ; and store Ρ{ω _πι \μ _Ιι ) as: Ρ{ω _πι \μ _Ιι ) = -f— end for end for

Aspects of the classification stage, as implemented by the classification module 210, will now be disclosed in detail.

The classification device 200 is configured to, during the classification stage, assign an unlabeled sequence, as defined by the input sequence x as obtained in step S102, to the correct class from the at least two classes {w _m }" ₌₁ . This corrected class is denoted o½*. As above, the input sequence x is assumed to consists of n, D dimensional, vectors x = {x _n }^ ₌₁ .

The classification device 200 is configured to determine a set of distances A _k from each data point x _n in the input sequence x to all clusters c _k . According to an embodiment there is one cluster contribution weight A _k for each input vector x _n . The classification device 200 is then configured to determine the cluster contribution weight A _k for input vector x _n by performing step Si04a:

Si04a: The classification device 200 determines one distance A _k between the input vector x _n and each of the clusters c _k . Step Si04a is performed as part of step S104.

Further, according to an embodiment each of the distances A _k is made inversely proportional to the median absolute deviation factor p _k relating to a spread of points inside cluster c _k . According to some aspects the distance between point x _n and cluster c _k is defined as:

For data point x _n this results in K distances {A _k } _k=1 . The inverse of these distances can be used to weigh the contribution of clusters in the probability density function estimation at point x _n . The distances {A _k } _k=1 from the data point x _n to all clusters c _k is used to create a weighted sum of the per-cluster posterior probabilities. This weighted sum serves as an estimate of posterior probabilities at the observation point x _n . According to an embodiment the classification device 200 is configured to determine the weighted sum

Ρ(ω _πι by performing step Si04b:

Si04b: The classification device 200 determines one cluster contribution weight A _k for each distance A _k . The cluster contribution weight A _k is inversely proportional to the distance A _k from which it is determined. Step Si04b is performed as part of step S104. Further, each cluster contribution weight A _fc can be inversely proportional to a sum based on all distances A _k . According to some aspects the cluster contribution weights A _k are determined as:

= _K -η (4)

Here, η is an expansion constant. Values that could be used for the expansion constant η are between 2 and 8, and are based on what property the non- parametric audio classification is be performed to classify and dimensionality of the input sequence x.

According to some aspects the classification device 200 is configured to estimate the posterior probabilities at point x _n from the pre-stored per- cluster posterior probabilities Ρ(ω _πι \μ _1{ ) and use the class contribution weights A _f c, as:

K

P{u _m \x _n ) = ^ λ _]ι Ρ{ω _πι \μ _]ι ), m = l, ... , M (5)

k=l

It is here noted that ^Js — as determined during the training stage is used to

^L k

represent Ρ{ω _πι | _¾ ).

Further, according to an embodiment there is one weighted sum Ρ(ω _πι per class ω _τη and per input vector x _n , and all weighted sums Ρ(ω _πι for one class ω _τη are combined over all input vector x _n to define one per-class posterior probability Ρ(ω _πι According to an embodiment the classification device 200 is configured to determine the per-class posterior probabilities Ρ(ω _πι for a given class ω _τη by performing step S104C:

S104C: The classification device 200 sums the weighted sum Ρ(ω _πι over all input vectors x _n for said given class ¾. Step S104C is performed as part of step S104. Logarithmic values of the weighted sum Ρ(ω _πι can be summed over all input vectors x _n to determine a logarithmic value of the per-class posterior probabilities Ρ(ω _πι for said given class ¾. That is, to assign the optimal class o½* to the input sequence x, the classification device 200 can be configured to determine the log-probability of the entire input sequence x (over all n observations) as follows

N

log(P(o _m |^)) = ^ \og(P (_a) _m \x _n )) (6)

71 =1

The classification device 200 is then configured to determine the class ω _τη that corresponds to the largest posterior as:

Mm* = argmax{\og(P(a) _m \x))} ^

ωπι

The optimal class o½* is thus found as the class ω _τη with largest posterior probability, given the input sequence x.

A non-limiting illustrative example of at least some of the herein disclosed embodiments will be provided next. A training sequence y represents data that belong to one of two classes ω , <¾ ₂ is generated, as illustrated in the probability density functions 100 and 110 in Fig. 1, where probability density function 100 represents class ω , and where probability density function 110 represents class ω ₂ . The cluster centers μ ₁ , μ ₂ , μ ₃ , and μ ₄ are identified at reference numerals 120a, 120b, 120c, and i2od and have values (from left to right in Fig. 1) 4.91, 8.89, 10.99, and !5-09, which are also reflected in the first column of Table 1.

The training stage of the presented algorithm obtains four clusters, and the corresponding posterior probabilities Ρ(ω _πι \μ _1{ ) as provided in Table 1.

Clusters Posterior probabilities μ ₁ = 4.91 _! = 1.36 Ρ{ω^ _μι ) =0.85 Ρ(ω ₂ | _! ) =0.15 c ₂ : μ ₂ = 8.89 P2 = 1.71 Ρ(ω ₁ \μ ₂ ) =0.43 Ρ(ω ₂ |2) = =0.57 3 = 10.99 p ₃ = 1.39 =0.56 Ρ(ω ₂ |μ ₃ ) =0.44 c ₄ : μ ₄ = 15.09 p ₄ = 1.71 Ρ(ω ₁ |μ ₄ ) = 0.12 Ρ(ω ₂ |μ ₄ ) =0.88

Table 1: Clusters and pre-stored posterior probabilities obtained from a training sequence.

For the classification stage it is assumed that a data point x _n = 4 (as identified by reference numeral 130 in Fig. 1) is observed and the task of the classification device 200 is to assign the data point x _n to one of the classes ω ₁ and ω ₂ .

The classification device 200 determines distances to all clusters by implementing Equation (3), and thus obtains _k (x _n , c _k ) = {0.6089, 13.9837, 35.1512, 71.9229}. The classification device 200 determines the cluster contributions to the given point x _n by implementing Equation (4) and obtains A _fc = {0.9977, 0.0019, 0.0003, 0.0001}. The classification device 200 determines the two posterior probabilities at point x _n by implementing Equation (5) and thus obtains Pteo xJ = 0.8491 and Ρ(ω ₂ |χ _η ) = 0.1509. The classification device 200 determines, by implementing Equations (6) and (7), that the point x _n = 4 belongs to class ω .

Fig. 3 schematically illustrates, in terms of a number of functional units, the components of a classification device 200 according to an embodiment.

Processing circuitry 310 is provided using any combination of one or more of a suitable central processing unit (CPU), multiprocessor, microcontroller, digital signal processor (DSP), etc., capable of executing software instructions stored in a computer program product 710 (as in Fig. 7), e.g. in the form of a storage medium 330. The processing circuitry 310 may further be provided as at least one application specific integrated circuit (ASIC), or field

programmable gate array (FPGA). Particularly, the processing circuitry 310 is configured to cause the

classification device 200 to perform a set of operations, or steps, S102-S106, as disclosed above. For example, the storage medium 330 may store the set of operations, and the processing circuitry 310 may be configured to retrieve the set of operations from the storage medium 330 to cause the classification device 200 to perform the set of operations. The set of operations may be provided as a set of executable instructions.

Thus the processing circuitry 310 is thereby arranged to execute methods as herein disclosed. The storage medium 330 may also comprise persistent storage, which, for example, can be any single one or combination of magnetic memory, optical memory, solid state memory or even remotely mounted memory. The classification device 200 may further comprise a communications interface 320 configured for communications with another device, for example to obtain the MFCCs as in step S102 and to provide a result of the classification as performed in step S106. As such the

communications interface 320 may comprise one or more transmitters and receivers, comprising analogue and digital components. The processing circuitry 310 controls the general operation of the classification device 200 e.g. by sending data and control signals to the communications interface 320 and the storage medium 330, by receiving data and reports from the communications interface 320, and by retrieving data and instructions from the storage medium 330. Other components, as well as the related

functionality, of the classification device 200 are omitted in order not to obscure the concepts presented herein.

Fig. 4 schematically illustrates, in terms of a number of functional modules, the components of a classification device 200 according to an embodiment. The classification device 200 of Fig. 4 comprises a number of functional modules; an obtain module 310a configured to perform step S102, a determine module 310b configured to perform step S104, and a classify module 310c configured to perform step S106. The classification device 200 of Fig. 4 may further comprises a number of optional functional modules, such as any of a determine module 3iod configured to perform step Si04a, a determine module 3ioe configured to perform step S104D, a sum module 3iof configured to perform step S104C, and an extract module 3iog configured to perform step Si02a. In general terms, each functional module 3ioa-3iog may in one embodiment be implemented only in hardware or and in another embodiment with the help of software, i.e., the latter embodiment having computer program instructions stored on the storage medium 330 which when run on the processing circuitry makes the classification device 200 perform the corresponding steps mentioned above in conjunction with Fig 4.

It should also be mentioned that even though the modules correspond to parts of a computer program, they do not need to be separate modules therein, but the way in which they are implemented in software is dependent on the programming language used. Preferably, one or more or all functional modules 3ioa-3iog may be implemented by the processing circuitry 310, possibly in cooperation with functional units 320 and/or 330. The processing circuitry 310 may thus be configured to from the storage medium 330 fetch instructions as provided by a functional module 3ioa-3iog and to execute these instructions, thereby performing any steps as will be disclosed herein.

The classification device 200 may be provided as a standalone device or as a part of at least one further device. For example, the classification device 200 may be provided in an audio mining device. Alternatively, functionality of the classification device 200 may be distributed between at least two devices, or nodes. These at least two nodes, or devices, may either be part of the same network part or may be spread between at least two such network parts.

Thus, a first portion of the instructions performed by the classification device 200 may be executed in a first device, and a second portion of the of the instructions performed by the classification device 200 may be executed in a second device; the herein disclosed embodiments are not limited to any particular number of devices on which the instructions performed by the classification device 200 may be executed. Hence, the methods according to the herein disclosed embodiments are suitable to be performed by a classification device 200 residing in a cloud computational environment. Therefore, although a single processing circuitry 310 is illustrated in Fig. 3 the processing circuitry 310 may be distributed among a plurality of devices, or nodes. The same applies to the functional modules 3ioa-3iog of Fig. 4 and the computer program 720 of Fig. 7 (see below).

Fig. 7 shows one example of a computer program product 710 comprising computer readable storage medium 730. On this computer readable storage medium 730, a computer program 720 can be stored, which computer program 720 can cause the processing circuitry 310 and thereto operatively coupled entities and devices, such as the communications interface 320 and the storage medium 330, to execute methods according to embodiments described herein. The computer program 720 and/or computer program product 710 may thus provide means for performing any steps as herein disclosed. In the example of Fig. 7, the computer program product 710 is illustrated as an optical disc, such as a CD (compact disc) or a DVD (digital versatile disc) or a Blu-Ray disc. The computer program product 710 could also be embodied as a memory, such as a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or an electrically erasable programmable read-only memory (EEPROM) and more particularly as a non-volatile storage medium of a device in an external memory such as a USB (Universal Serial Bus) memory or a Flash memory, such as a compact Flash memory. Thus, while the computer program 720 is here schematically shown as a track on the depicted optical disk, the computer program 720 can be stored in any way which is suitable for the computer program product 710.

The inventive concept has mainly been described above with reference to a few embodiments. However, as is readily appreciated by a person skilled in the art, other embodiments than the ones disclosed above are equally possible within the scope of the inventive concept, as defined by the appended patent claims.