Patent Description:
Packet loss concealment (PLC) is used in audio codecs to conceal lost or corrupted packets during the transmission from the encoder to the decoder. PLC is performed at the decoder side and works by extrapolating the decoded signal either in the transform-domain or in the time-domain. Ideally, the concealed signal should be artifact-free and should have the same spectral characteristics as the missing signal.

Error robust audio codecs, as described in [<NUM>] and [<NUM>], have generally multiple concealment methods for the various signal types like speech as an example for a monophonic signal, music as an example for polyphonic signal or noise signal. The selection is based on a set of signal features, which are either transmitted and decoded from the bit stream or estimated in the decoder.

Pitch-based PLC techniques generally produce good results for speech and monophonic signals. These approaches assume that the signal is locally stationary and recover the lost signal by synthesizing a periodic signal using an extrapolated pitch period. These techniques are widely used in CELP-based speech coding such as in ITU-T G. <NUM> [<NUM>]. They can also be used for PCM coding such as in ITU-T G. <NUM> [<NUM>] and more recently they were applied to MDCT-based audio coding, the best example being TCX time domain concealment, TCX TD-PLC, in the 3GPP EVS standard [<NUM>].

The pitch-lag is the main parameter used in pitch-based PLC. This parameter can be estimated at the encoder-side and encoded into the bit stream. In this case, the pitch-lag of the last good frame is used to conceal the current lost frame such as in [<NUM>] and [<NUM>]. If there is no pitch-lag in the bitstream, it can be estimated at the decoder-side by running a pitch detection algorithm on the decoded signal such as in [<NUM>].

For non-periodic, non-tonal, noise-like signals, a low complexity technique called frame repetition with sign scrambling has been found to be effective. It is based on repeating the last frame and multiplying the spectral coefficients with a randomly generated sign to conceal the lost frame. One example of MDCT frame repetition with sign scrambling can be found in the 3GPP EVS standard [<NUM>].

For tonal polyphonic signals or complex music signals a method is used which is based on predicting the phase of the spectral coefficients of any detected tonal component. This method shows a consistent improvement for stationary tonal signals. A tonal component consists of a peak that also existed in the previous received frame(s). The phase of the spectral coefficients belonging to the tonal components is determined from the power spectrum of the last received frame(s). One example of tonal MDCT concealment can be found in the 3GPP EVS standard [<NUM>].

Summarizing the above, different PLC methods are known but they are specific for certain situations, i.e., for certain audio characteristics. That is, an audio coder supporting several of these PLC methods should have a mechanism to choose the most suitable PLC method at the time of encountering frame or packet loss. The most suitable PLC method is the one leading to the least noticeable substitute for the lost signal.

In <CIT>, a system and method for performing frame loss concealment (FLC) when portions of a bit stream representing an audio signal are lost are described within the context of a digital communication system. The system and method utilizes a plurality of different FLC techniques, wherein each technique is tuned or designed for a different kind of audio signal. When a frame is lost, a previously-decoded audio signal corresponding to one or more previously-received good frames is analyzed. Based on the result of the analysis, the FLC technique that is most likely to perform well for the previously-decoded audio signal is chosen to perform the FLC operation for the current lost frame. In one implementation, the plurality of different FLC techniques include an FLC technique designed for music, such as a frame repeat FLC technique, and an FLC technique designed for speech, such as a periodic waveform extrapolation (PWE) technique.

The object of the present application is to provide an audio decoding concept which allows an audio decoder which comprises a set of different loss concealment tools to achieve an improved loss concealment.

This object is achieved by the subject matter of the independent claims of the present application.

The idea of the present invention is based on the finding that an assignment of one out of a set of different loss concealment tools of an audio decoder to a portion of the audio signal to be decoded from a data stream, which portion is affected by loss, that is the selection out of the set of different loss concealment tools, may be made in a manner leading to a more pleasant loss concealment if the assignment/selection is done based on two measures: A first measure which is determined measures a spectral position of a spectral centroid of a spectrum of the audio signal and a second measure which is determined measures a temporal predictability of the audio signal. The assigned or selected loss concealment tool may then be used to recover the portion of the audio signal.

For instance, based on the aforementioned first and second measures, one of first and second loss concealment tools may be assigned to the lost portion with a first loss concealment tool being configured to recover the audio signal by audio signal synthesis using a periodic signal of a periodicity which depends on a pitch value derived from the data stream, and the second loss concealment tool may be configured to recover the audio signal by detecting tonal spectral components of the audio signal, performing phase detection at the tonal spectral components and audio signal synthesis by combining the signals of periodicities which depend on the tonal spectral components at adjustment of a mutual phase shift between the signals depending on the phase detection. In other words, based on the first and second measures, one of a tonal frequency domain PLC tool and a tonal time domain PLC tool may be assigned to the lost portion.

In accordance with an embodiment, the assignment/selection of a PLC tool for a lost portion is performed in stages: A third measure measuring a tonality of the spectrum of the audio signal is determined and one out of a first and a second subset of one or more loss concealment tools out of the set of different loss concealment tools is assigned to the lost portion, and merely if the first subset of one or more loss concealment tools is assigned to the lost portion, the assignment of the one PLC tool for the lost portion out of this first subset is performed based on the first and second measures. Otherwise, the assignment/selection is performed out of the second subset.

Advantageous aspects of the present application are the subject of dependent claims. Preferred embodiments of the present application are set out below with respect to the figures among which:.

<FIG> shows an audio decoder in accordance with an embodiment of the present application. As shown therein, the audio decoder <NUM> is configured to decode an audio signal <NUM> from a data stream <NUM>. The audio signal <NUM> may be encoded into data stream <NUM> in accordance with any suitable audio codec such as a time-domain based audio codec or a frequency-domain audio codec. The audio signal <NUM> may be coded into data stream <NUM> in units of temporal portions <NUM> of frames <NUM>, respectively. To be more precise, the audio signal <NUM> may be temporally sub-divided into overlapping or non-overlapping temporal portions or intervals <NUM>, each of which corresponds to a certain one of frames <NUM> which data stream <NUM> is sub-divided into. Each frame <NUM> encodes a corresponding temporal portion <NUM>. For instance, a portion <NUM> may contain information on linear prediction coefficients describing a spectral envelope of the audio signal within the corresponding portion <NUM>. Additionally, frame <NUM> may have encoded thereinto spectral coefficients describing a spectrum of the audio signal <NUM> within portion <NUM> which is to be shaped, for instance, by audio decoder <NUM> according to the linear prediction coefficients contained in that frame. An overlap add process might also be applied by the audio decoder <NUM> in reconstructing the audio signal <NUM> from the data stream <NUM>. Naturally, the possibilities would also apply with the examples presented herein merely serving for ease of understanding.

The data stream <NUM> may be received by the audio decoder <NUM> in a packetized form, i.e., in units of packets. The sub-division of data stream <NUM> into frame <NUM> itself represents a kind of packetization, i.e., the frames <NUM> represent packets. Additionally, data stream <NUM> may be packed into packets of a transport stream or media file format, but this circumstance is not inspected in further detail here. Rather, it should suffice to state that the reception of the data stream <NUM> by audio decoder <NUM> is liable to data or signal loss, called packet loss in the following. That is, some continuous portion <NUM> of the data stream <NUM> might have got lost during transmission, thus not received by audio decoder <NUM>, so that the corresponding portion is missing and not available for the audio decoder <NUM>. As a consequence, audio decoder <NUM> misses information in the data stream <NUM> so as to reconstruct a portion <NUM> corresponding to portion <NUM>. In other words, the audio decoder <NUM> is not able to reconstruct portion <NUM> from data stream <NUM> in accordance with a normal audio decoding process implemented, for instance, in an audio decoding core <NUM> of the audio decoder, as portion <NUM> of data stream <NUM> is missing. Rather, in order to deal with such missing portions <NUM>, audio decoder <NUM> comprises a set <NUM> of PLC tools <NUM> so as to recover or synthesize the audio signal <NUM> within portion <NUM> by a substitute signal <NUM>. The PLC tools <NUM> comprised by set <NUM> differs in their suitability for different audio signal characteristics. That is, the degree of annoyance when using a certain PLC tool for the recovery of a signal substitute <NUM> within a certain portion <NUM> of the audio signal <NUM> depends on the audio signal characteristic at that portion <NUM> and PLC tools <NUM> within set <NUM> show mutually different degrees of annoyance for a certain set of audio signal characteristics. Accordingly, audio decoder <NUM> comprises an assigner <NUM> which assigns one of the set <NUM> of packet loss concealment tools <NUM> to portion <NUM> of the audio signal <NUM> which is affected by a packet loss such as the lost portion <NUM> of data stream <NUM>. The assigner <NUM> tries to assign the best PLC tool <NUM> to portion <NUM>, namely the one which leads to the lowest annoyance.

Once the assigner <NUM> has assigned a certain PLC tool <NUM> to a lost portion <NUM> of the audio signal <NUM>, the audio decoder <NUM> recovers this portion <NUM> of the audio signal using the assigned PLC tool <NUM>, thereby substituting the audio signal <NUM> within this portion <NUM>, as it would have been reconstructed from the audio data stream <NUM> if the corresponding data stream portion <NUM> would not have got lost, by a substitute signal <NUM> obtained using the PLC tool <NUM> assigned for portion <NUM> by assigner <NUM>.

As already indicated above, the assignment of a particular PLC tool <NUM> to a certain lost portion <NUM> should be made signal dependent in order to render the lost concealment as least annoying as possible. Signal dependency, however, is restricted to portions of data stream <NUM> preceding the lost data stream portion <NUM> and, in accordance with the embodiment described herein, the assigner <NUM> acts as follows.

In order to explain this in more detail, reference is made to <FIG> shows that the whole assignment process for a certain missing portion <NUM> might possibly be triggered by a loss detector <NUM> possibly present in audio decoder <NUM>. In particular, if lost detection <NUM> performed by loss detector <NUM> reveals that some portion <NUM> of data stream <NUM> is missing or lost, as checked at <NUM>, the following assignment process is triggered. A determination <NUM> is performed in order to determine a first measure <NUM> which measures a spectral position of a spectral centroid of a spectrum of the audio signal. That is, assigner <NUM> determines a spectral position of a center of mass of a spectrum of the audio signal, see <FIG>. The audio decoder retrieves from a portion <NUM> preceding the lost portion <NUM> of data stream <NUM>, preceding in data stream order, a spectrum <NUM> of the audio signal. As described above with respect to <FIG>, it might be that data stream <NUM> has audio signal <NUM> encoded thereinto in spectral domain anyway so that no spectral decomposition is necessary for assigner <NUM> to obtain spectrum <NUM>. For instance, spectral coefficients of a most recently received frame <NUM> or more than one most recently retrieved frame <NUM> of data stream <NUM>, prior to the lost portion <NUM>, is used to obtain spectrum <NUM>. If more than one frame <NUM> is used, it could be that spectrum <NUM> used by assigner <NUM> is obtained by averaging. In order to perform the determination <NUM>, the center of mass of this spectrum <NUM> is determined, i.e., a measure <NUM> measuring a spectral position <NUM> of spectrum <NUM>. Later on, a specific example is presented.

Further, the assignment process triggered by loss detection comprises a determination <NUM> of a temporal predictability of the audio signal so as to obtain a measure <NUM> of this temporal predictability, see <FIG> for details. As shown therein, in accordance with an embodiment, the temporal predictability detection <NUM> may rely on the decoded signal or the audio signal <NUM> as derived from the data stream <NUM> up to the signal portion <NUM> which is missing owing to the loss of portion <NUM> of data stream <NUM>. In other words, the temporal predictability detection <NUM> may be based on the audio signal <NUM> within portion <NUM> which immediately precedes lost portion <NUM>, the loss of which is to be concealed, and which is decoded from portion <NUM> of data stream <NUM>. The temporal predictability detection <NUM> may be done in a manner so that measure <NUM> is a measure for a self-similarity or autocorrelation of the signal portion <NUM> as illustrated at <NUM> in <FIG>. The mutual shift s for which the self-similarity of signal <NUM> is measured by measure <NUM> may be determined by assigner <NUM> in different manners. For instance, assigner <NUM> may inspect a corresponding pitch parameter conveyed in one or more of the frames <NUM> within portion <NUM> preceding lost portion <NUM> of data stream <NUM>. That is, the mutual shift s at which the self-similarity may be measured may correspond to a pitch period with a pitch being determined based on a parameter in data stream <NUM>, namely portion <NUM>. The self-similarity or correlation <NUM> at that pitch period shift may be used as the second measure <NUM>.

It is obvious that the order of performing determination <NUM> and <NUM>, respectively, may be switched or that both detections may be performed concurrently. Based on measures <NUM> and <NUM>, an assignment <NUM> is performed. This assignment <NUM> selects one out of two PLC tools <NUM> for concealment of loss of portion <NUM>. This PLC tool, i.e., the assigned one <NUM>, is then used for the concealment of the loss of portion <NUM>.

As a brief note, it should be noted that the number of PLC tools <NUM>, between which the selection by assignment <NUM> is performed, may be greater than two.

In accordance with an embodiment further outlined below, however, the PLC tool PLC#<NUM> of <FIG> may be described as <NUM> using which the substitute signal <NUM>, i.e., the audio signal estimate within portion <NUM>, is obtained or recovered using tonal time domain packet loss concealment. In other words, PLC#<NUM> may be a packet loss concealment dedicated for audio signal recovery of monophonic portions. PLC#<NUM> may recover an audio signal within a missing portion <NUM> of an audio signal <NUM> using a periodic signal of a periodicity which depends on a pitch parameter or pitch value derived from the data stream, namely from portion <NUM> of data stream <NUM>, i.e., the portion <NUM> preceding the lost portion <NUM> of data stream <NUM>.

The second PLC tool PLC#<NUM> may be dedicated for the recovery of audio signals of polyphonic type. The concealment of this second PLC tool PLC#<NUM> may be based on tonal frequency domain packet loss concealment.

With respect to <FIG> and <FIG>, a possible implementation of PLC#<NUM> and PLC#<NUM> will be briefly explained.

<FIG> illustrates PLC#<NUM>. A pitch parameter or pitch value <NUM> conveyed in a frame <NUM> within portion <NUM> preceding lost portion <NUM> of data stream <NUM> is used to set a periodicity or period length <NUM> of a periodic signal <NUM> which is then used to form a substitute or used to conceal the loss within portion <NUM> of audio signal <NUM>. The pitch parameter or pitch value <NUM> may be present in data stream <NUM> in order to be used by audio decoder <NUM> in case of normal audio decoding, i.e., no signal loss, for controlling, for instance, a harmonic filter tool or the like. That is, parameter <NUM> may be present in data stream <NUM> anyway. Otherwise, PLC tool <NUM> performing PLC#<NUM> according to <FIG> could determine the pitch period <NUM> by analysis such as by analyzing the decoded signal <NUM> in front of the lost portion <NUM> or by analyzing the most recent accessible version of the spectrum such as spectrum <NUM> depicted in <FIG>.

<FIG> illustrates PLC#<NUM> in accordance with an embodiment. Here, the PLC tool <NUM>, responsible for performing PLC#<NUM>, uses, for instance, one or two or more most recently obtained spectra as obtained from portion <NUM> of data stream <NUM> so as to detect or determine tonal spectral components therein, i.e., peaks <NUM> in the spectrum <NUM> or those peaks <NUM> in spectrum <NUM> which occurr at that position or a sufficiently similar position in the spectrum of a certain number of consecutive spectra or frames <NUM>, respectively. Sufficiently similar positions may be those the spectral distance of which is below a certain threshold. The spectral positions of the peaks <NUM> represent the tonal spectral components and here, at these spectral locations, the phase detection is performed by use of, for instance, or by evaluation of, for instance, a power spectrum of the audio signal. Then, within the temporal portion <NUM> within which the signal loss is to be concealed, a combination of signals, periodicities of which depend on the tonal spectral components, is formed so as to yield the supplement signal <NUM>, wherein a mutual phase shift between the combined signals is adjusted depending on the phase detection. For instance, a phase is determined for each tonal component <NUM> or merely phase differences between these tonal components are determined, and a signal is formed as the substitute <NUM> within portion <NUM> which is synthesized by these tonal components <NUM> with obeying the phase differences or phases. The combination may be formed in spectral domain with deriving the substitute signal <NUM> by inverse transformation, or in time-domain directly by adding, for instance, appropriately mutually shifted signals, the mutual shift reflecting the determined mutual phase shift.

As described in more detail below, the assignment <NUM> may be done in a manner so that PLC#<NUM> is chosen or assigned to portion <NUM> the more likely the lower the spectral position <NUM> is and the higher the temporal predictability is and, vice versa, PLC#<NUM> is assigned or selected the more likely the higher the spectral position <NUM> is and the lower the temporal predictability is. A higher spectral position corresponds to a higher frequency and a lower spectral position to a lower frequency. By doing this in this manner, PLC#<NUM> is more likely chosen in case of portion <NUM> corresponding to lost speech and PLC#<NUM> is more likely selected in case of portion <NUM> relating to polyphone signals or music.

For the sake of completeness, <FIG> shows the fact that the assignment process of <FIG> may be extended. In particular, as shown in <FIG>, the assignment <NUM> has been done by restricting the assignment or selection of assignment <NUM> onto a subset <NUM> of PLC tools <NUM>. The set <NUM> of PLC tools <NUM> may contain a further subset of one or more PLC tools <NUM>, such as subset <NUM> and, when triggered by loss detection <NUM>, a tonality detection <NUM> may be used by assigner <NUM> first in order to determine whether portion <NUM> relates to a tonal portion or not. The tonality determination <NUM> yields a tonality measure or indicator <NUM> and this tonality measure indicator <NUM> may be obtained in <NUM> by use of one or more parameters within portion <NUM> of data stream <NUM>, for instance such as by inspecting whether or not a most recent frame <NUM> within portion <NUM> comprises a certain pitch parameter such as a pitch value <NUM> as described in <FIG>. In case of absence, this may be interpreted as an indication that the audio signal is currently non-tonal and, in case of presence of the pitch parameter <NUM>, this may be interpreted as indicating that the audio signal is currently tonal. This indication is then the tonality measure <NUM>. Using measure <NUM> it is discriminated at <NUM> whether the audio signal is tonal or not and, if it is non-tonal, the PLC tool <NUM> assigned to portion <NUM> is assigned out of subset <NUM>. <FIG> illustrates the case where one PLC tool <NUM> is comprised by subset <NUM> and this one is chosen. However, even here, a further selection out of subset <NUM> may follow. If tonality is confirmed at <NUM>, the assignment <NUM> based on determinations <NUM> and <NUM> is performed with respect to subset <NUM> as described above with respect to <FIG>.

PLC#<NUM> may be a non-tonal PLC such as a PLC which recovers an audio signal for a portion <NUM> by use of frame repetition with or without replicate modification, when the replicate modification may, as indicated above, involve sign scrambling, i.e., a random sign flip of spectral coefficients of a most recently received spectrum such as spectrum <NUM> which is then inversely transformed and used to derive substitute signal <NUM>.

<FIG> visualizes a decision tree for selecting among the PLC tools <NUM> in accordance with a manner which corresponds, for instance, to <FIG>. Decisions A and B are made to decide which of three PLC methods PLC#<NUM> to PLC#<NUM>, which are designed for different signal types, are to be used for a certain missing signal portion <NUM> in order to get the best concealment performance. The first decision, decision A, is based on tonality. If the signal turns out to be non-tonal, PLC#<NUM> is used for concealment. If tonal, decision B is made. Decision B checks the tonal signal characteristics based on measures <NUM> and <NUM> in the manner described above with respect to <FIG> and <FIG> and, depending on the characteristics, chooses one of PLC#<NUM> or PLC#<NUM>. As explained above, PLC#<NUM> may be a tonal time domain PLC for monophone and/or speech signals, while PLC#<NUM> may be a tonal frequency domain PLC for a polyphone and/or music signals.

The decision tree of <FIG>, thus, visualizes decisions, which may be taken between three PLC methods for different signal types to get the best concealment performance. The decision A, which may correspond to check <NUM> of <FIG>, may be done by inspecting a signal type classification, i.e. by using it as an indication of, or by deriving therefrom a tonality indicator. The signal type classification is possibly present in each frame <NUM> and indicates a frame class of each frame. It could be calculated on encoder side and transmitted in the bitstream <NUM> to the audio decoder. Even alternatively, it could be calculated on decoder side. However, the calculation of the frame class is very complex and may require that all features are calculated in every frame due to a frame dependency of the frame class. Therefore, for low-complexity applications, it may be preferred if a simpler approach is used. As indicated above, the presence or absence of some pitch parameter <NUM> may be used as indicator <NUM>.

The decision B which corresponds to assignment <NUM> based on determinations <NUM> and <NUM>, yields a good choice between PLC#<NUM> and PLC#<NUM>. In [<NUM>], such a choice has been done based on a stability measurement of the spectral envelope, which correlates to the short-term stationarity of the signal. However, the more stationary a signal is, the better is the performance of both tonal PLC methods PLC#<NUM> and PLC#<NUM>. This means stationarity is, hence, not a suitable criterion to select the optimal tonal concealment method. The stationarity feature indicates tonality very well, however it cannot differentiate between speech/monophonic and polyphonic/music.

As discussed above, it is possible, to perform the decision tree of <FIG> using a PLC classifier represented by assigner <NUM>, which may operate on a frame-by-frame basis without any inter-frame dependencies and thus necessitates merely a low complexity. It may calculate its classification features <NUM> and <NUM> only in case of a frame loss, as detected or checked at <NUM>, and therefore does not add an immanent complexity offset in the error-free frames among frames <NUM>.

The decision A may be done based on a tonality indicator <NUM>, which can be the presence of a pitch value in the last good received audio frame. The decision B may be done by using the spectral centroid <NUM> and a long term prediction gain <NUM> calculated on the last good received audio frame.

The decision B may switch between a pitch-based time domain concealment method PLC#<NUM>, best suited for monophonically and speech-like signals, and frequency domain methods PLC#<NUM>, best suited for polyphone or complex music signals. An advantage of the classification of decision B results from the fact, that:.

Therefore, a weighted combination of both features <NUM> and <NUM> may be used for decision B and assignment process <NUM> and results in a reliable discrimination of speech/monophonic and polyphonic/complex music signals. At the same time, the complexity may be kept low.

If the audio decoder receives a corrupted frame or if the frame is lost, i.e. encounters a lost portion <NUM>, as detected at <NUM>, the following may be done, wherein reference is also made to <FIG>:.

For a positive decision A, the features <NUM> and <NUM> may be calculated based on the last good frame in the following manner:.

The two calculated features are combined with the following formula: <MAT> where w<NUM>, w<NUM> and β are weights. In one embodiment, these are <MAT>, w<NUM> = -<NUM> and <MAT>. Alternatives are setting w<NUM>, w<NUM> and β so that <MAT>, and <MAT>. The weights may be normalized here to be in the range [-<NUM>:<NUM>].

Then, the PLC#<NUM>, e.g. time domain pitch-based PLC method, may be chosen if class > <NUM> in <NUM> and PLC#<NUM>, such as a frequency domain tonal concealment, otherwise.

Some notes shall be made with respect to the above description. For instance, the spectrum, the spectral centroid of which is measured to obtain the first measure <NUM>, might be a so called weighted version such as a pre-emphasized version. Such weighting is used, for instance, to adapt the quantization noise to the psychoacoustic masking threshold. In other words, it might be that the first measure <NUM> measuring a spectral position <NUM> of a spectral centroid of a psychoacoustically scaled spectrum of the audio signal. This might be especially advantageous in cases where the normal audio decoding codec underlying audio decoding core <NUM> involves that data stream <NUM> has audio signal <NUM> encoded thereinto in spectral domain anyway, namely in the weighted domain. Additionally or alternatively, the spectrum, the spectral centroid of which is measured to obtain the first measure <NUM>, is not necessarily one represented at a spectral resolution as high as the spectral resolution used in the audio decoding core <NUM> to transition to time domain. Rather, it may be higher or lower. Even additionally or alternatively, it should be noted that the audio signal's spectrum also manifests itself in scale factors. Such scale factors might be transmitted in the data stream <NUM> along with spectral coefficients in order to, together, form a coded representation of the audio signal's spectrum. For a certain portion <NUM>, the spectral coefficients are scaled according to the scale factors. There are more spectral coefficients than scaler factors. Each scale factor, for instance, is assigned to one of several spectral bands, so called scale factor bands, into which the audio signal's bandwidth is partitioned. The scale factors, thus, define the spectrum of the audio signal for a certain portion in terms of envelope at some spectral resolution reduced compared to the one at which the quantized spectral coefficients are coded in the data stream <NUM>. It could even be that the spectral resolution at which the scale factors are coded in the data stream <NUM> is even lower than a spectral resolution at which the decoding core <NUM> performs the dequantization of the spectral coefficients. For instance, the decoding core <NUM> might subject the scale factors coded into the data stream <NUM> to spectral interpolation to obtain interpolated scale factors of higher spectral resolution as the ones coded into the data stream, and use the interpolated scale factors for dequantization. Either one of the scale factors coded into the data stream and the interpolated scale factors might be used as the spectrum of the audio signal the spectral centroid of which is measured by the first measure <NUM>. This means that centroid measurement becomes quite computational efficient to be determined as the number of computational operations to be performed to determine the first measure is low compared to performing the centroid measurement at any higher resolution such as at the one at which the spectral coefficient are coded or some other resolution in case of obtaining the spectrum for the centroid measurement by subjecting the decoded audio signal to an extra spectral decomposition which would even further increase the efforts. Thus, as a concrete example, first and second measures could be computed as follows based on coded down-sampled scale factors SNS (spectral noise shaping):
Firstly, a pitch value Tc might be computed as a basis: <MAT> where pitch_present and pitch_int are bitstream parameters derived by the decoder from the last good frame. pitch_present can be interpreted as a tonality indicator.

As the second measure, a long term prediction gain xcorr might be computed according to: <MAT> where x(k), k = <NUM>. N - <NUM> are the last decoded time samples and N can be a predetermined length value such as limited value like the maximum pitch value or a frame length NF (for example <NUM>), for example <MAT> where pitmin is the minimal pitch value. Thus, the second measure would be computed as the self-similarity of the decoded audio time signal at the most recently received portion with itself, mutually shifted at the pitch.

As the second measure, a spectral centroid sc could be computed as: <MAT> where fs is the sampling rate and <MAT> and Ifs are non-uniform band indices, i.e. band indices defining for each band the lower and upper frequency border in a manner so that the band widths defined by the difference between the associated lower and upper border differ from each other such as increase with increasing frequency although the difference is optional. The band indices might be defined in dependency of the sampling rate/frequency of the audio signal. Further, <MAT> where scfQ-<NUM>(k) is the scale factor vector stored in the bitstream of the last good frame and gtilt is a predetermined tilt factor which might be set by default and, possibly, depending on the sample frequency of the audio signal. The term <NUM>scfQ-<NUM>(k) is applied to get the scalefactors coded in the logarithmic domain back in the linear domain. The term <MAT> is applied to inverse the encoder side pre-emphasis filter, which is called de-emphasis filter. The scale factor vector is calculated at encoder side and transmitted in the bitstream. It is determined on the energies per band of the MDCT coefficients, where the bands are non-uniform and follow the perceptually-relevant bark scale (smaller in low-frequencies, larger in high-frequencies). After smoothing, pre-emphasing and transforming the energies to logarithmic domain, they are, at the encoder side, downsampled from <NUM> parameters to <NUM> parameters to form the scale factor vector, which afterwards is coded and transmitted in the bitstream. Thus, sc is a measure for a spectral position <NUM> of a spectral centroid of a spectrum <NUM> of the audio signal, here determined based on the spectrally coarse sampled version thereof, namely the SNS parameters.

The decision or selection among the various PLC methods may then be done with the criteria xcorr and sc. Frame repetition with sign scrambling might be selected if Tc = <NUM> (which means that the tonality indicator pitch_present = <NUM>). Otherwise, the value class is calculated as follows: <MAT> time domain pitch-based PLC method might be chosen if class > <NUM>; frequency domain tonal concealment otherwise.

Thus, an audio decoder for decoding an audio signal <NUM> from a data stream <NUM>, which comprises a set <NUM> of different loss concealment tools <NUM> might be configured to determine <NUM> a first measure <NUM> measuring a spectral position <NUM> of a spectral centroid of a spectrum <NUM> of the audio signal by deriving the spectrum from scale factors in a most recent non-lost portion of the data stream, determine <NUM> a second measure <NUM> measuring a temporal predictability of the audio signal, assign <NUM> one <NUM> of the set <NUM> of different loss concealment tools <NUM> to a portion <NUM> of the audio signal <NUM> affected by loss based on the first and second measures, and recover the portion <NUM> of the audio signal using the one loss concealment tool <NUM> assigned to the portion <NUM>. The derivation of the spectrum might involve, as described, subjecting the scaler factors coded in the data stream to spectral interpolation. Additionally or alternatively, they may be subject to de-emphasis filtering, i.e. they might be multiplied by a de-emphasis filter's transfer function. The resulting scale factors may then be subject to spectral centroid measurement. All the other details described above may then be applied as well. In any case, the set <NUM> of different loss concealment tools comprises a first loss concealment tool for audio signal recovery of monophonic portions, and a second loss concealment tool for audio signal recovery of polyphonic portions, and the audio decoder is configured to, in assigning the one of the set of different loss concealment tools to the portion of the audio signal based on the first and second measures, assign the first loss concealment tool to the portion the more likely the lower the spectral position of the spectral centroid is and the higher the temporal predictability is, and assign the second loss concealment tool to the portion the more likely the higher the spectral position of the spectral centroid is and the lower the temporal predictability is. Additionally or alternatively, the audio decoder may be configured to, in assigning one of the set of different loss concealment tools to a portion <NUM> of the audio signal affected by loss based on the first and second measures, perform a summation over the first and second measures <NUM>, <NUM> so as to obtain a scalar sum value and subjecting the scalar sum value to thresholding.

Claim 1:
Audio decoder for decoding an audio signal (<NUM>) from a data stream (<NUM>), the audio decoder comprising a set (<NUM>) of different loss concealment tools (<NUM>) and configured to
determine (<NUM>) a first measure (<NUM>) measuring a spectral position (<NUM>) of a spectral centroid of a spectrum (<NUM>) of the audio signal,
determine (<NUM>) a second measure (<NUM>) measuring a temporal predictability of the audio signal, wherein the second measure is a measure for self-similarity or autocorrelation or is a long term prediction gain,
assign (<NUM>) one loss concealment tool (<NUM>) of the set (<NUM>) of different loss concealment tools (<NUM>) to a portion (<NUM>) of the audio signal (<NUM>) affected by loss based on the first and second measures, and
recover the portion (<NUM>) of the audio signal using the one loss concealment tool (<NUM>) assigned to the portion (<NUM>),
wherein the set (<NUM>) of different loss concealment tools comprises
a first loss concealment tool (PLC#<NUM>) for audio signal recovery of monophonic portions, and
a second loss concealment tool (PLC#<NUM>) for audio signal recovery of polyphonic portions,
wherein the audio decoder is configured to, in assigning the one loss concealment tool to the portion of the audio signal based on the first and second measures, assign the first loss concealment tool to the portion the more likely the lower the spectral position of the spectral centroid is and the higher the temporal predictability is, and assign the second loss concealment tool to the portion the more likely the higher the spectral position of the spectral centroid is and the lower the temporal predictability is.