Apparatus And Method For Determining A Measure For A Perceived Level Of
Reverberation, Audio Processor And Method For Processing A Signal
Specification
The present application is related to audio signal processing and, particularly, to audio
processing usable in artificial reverberators.
The determination of a measure for a perceived level of reverberation is, for example,
desired for applications where an artificial reverberation processor is operated in an
automated way and needs to adapt its parameters to the input signal such that the perceived
level of the reverberation matches a target value. It is noted that the term revcrberance
while alluding to the same theme, does not appear to have a commonly accepted definition
which makes it difficult to use as a quantitative measure in a listening test and prediction
scenario.
Artificial reverberation processors are often implemented as linear time-invariant systems
and operated in a send-return signal path, as depicted in Fig. 6, with pre-delay d ,
reverberation impulse response (R1R) and a scaling factor g for controlling the direct-toreverberation
ratio (DRR). When implemented as parametric reverberation processors,
they feature a variety of parameters, e.g. for controlling the shape and the density of the
RIR, and the inter-channel coherence (ICC) of the RIRs for multi-channel processors i
one or more frequency bands.
Fig. 6 shows a direct signal x[k] input at an input 600, and this signal is forwarded to an
adder 602 for adding this signal to a reverberation signal component r[k] output from a
weighter 604, which receives, at its first input, a signal output by a reverberation filter 606
and which receives, at its second input, a gain factor g . The reverberation filter 606 may
have an optional delay stage 608 connected upstream of the reverberation filter 606, but
due to the fact that the reverberation filter 606 will include some delay by itself, the delay
in block 608 can be included in the reverberation filter 606 so that the upper branch in Fig.
6 can only comprise a single filter incorporating the delay and the reverberation or only
incorporate the reverberation without any additional delay. A reverberation signal
component is output by the filter 606 and this reverberation signal component can be
modified by the multiplier 606 in response to the gain factor g in order to obtain the
manipulated reverberation signal component r[k] which is then combined with the direct
signal component input at 600 in order to finally obtain the mi signal m[k at the output of
the adder 602. It s noted that the term ''reverberation filter" refers to common
implementations of artificial reverberations (either as convolution which is equivalent to
FIR filtering, or as implementations using recursive structures, such as Feedback Delay
Networks or networks of allpass filters and feedback comb filters or other recursive filters),
but designates a general processing which produces a reverberant signal. Such processings
may involve non-linear processes or time varying processes such as low-frequent
modulations of signal amplitudes or delay lengths. In these cases the term "reverberation
filter" would not apply in a strict technical sense of an Linear Time Invariant (LTI) system.
In fact, the "reverberation filter" refers to a processing which outputs a reverberant signal,
possibly including a mechanism for reading a computed or recorded reverberant signal
from memory.
These parameters have a impact on the resulting audio signal in terms of perceived level,
distance, room size, coloration and sound quality. Furthermore, the perceived
characteristics of the reverberation depend on the temporal and spectral characteristics of
the input signal [1]. Focusing on a very important sensation, namely loudness, it can be
observed that the loudness of the perceived reverberation is monotonically related to the
non-stationarity of the input signal. Intuitively speaking, a audio signal with large
variations in its envelope excites the reverberation at high levels and allows it to become
audible at lower levels. In a typical scenario where the long-term DRR expressed in
decibels is positive, the direct signal can mask the reverberation signal almost completely
at time instances where its energy envelope increases. On the other hand, whenever the
signal ends, the previously excited reverberation tail becomes apparent in gaps exceeding a
minimum duration determined by the slope of the post-masking (at maximum 200 ms) and
the integration time of the auditory system (at maximum 200 ms for moderate levels).
To illustrate this, Fig. 4a shows the time signal envelopes of a synthetic audio signal and
of an artificially generated reverberation signal, and Fig. 4b shows predicted loudness and
partial loudness functions computed with a computational model of loudness. An RIR with
a short pre-delay of 50 ms is used here, omitting early reflections and synthesizing the late
part of the reverberation with exponentially decaying white noise [2]. The input signal has
been generated from a harmonic wide-band signal and an envelope function such that one
event with a short decay and a second event with a long decay are perceived. While the
long event produces more total reverberation energy, it comes to no surprise that it is the
short sound which is perceived as being more reverberant. Where the decaying slope of the
longer event masks the reverberation, the short sound already disappeared before the
reverberation has built up and thereby a gap is open in which the reverberation is
perceived. Please note that the definition of masking used here includes both complete and
partial masking [3].
Although such observations have been made many times [4, 5, 6], it is still worth
emphasizing them because it illustrates qualitatively why models of partial loudness can be
applied in the context of this work. In fact, it has been pointed out that the perception of
reverberation arises from stream segregation processes in the auditory system [4, 5, 6] and
is influenced by the partial masking of the reverberation due to the direct sound.
The considerations above motivate the use of loudness models. Related investigations were
performed by Lee et al. and focus on the prediction of the subjective decay rate of RIRs
when listening to them directly [7] and on the effect of the playback level on reverberance
[8]. A predictor for reverberance using loudness-based early decay times is proposed in
[9]. I contrast to this work, the prediction methods proposed here process the direct signal
and the reverberation signal with a computational model of partial loudness (and with
simplified versions of it in the quest for low-complexity implementations) and thereby
consider the influence of the input (direct) signal on the sensation. Recently, Tsilfidis and
Mourjopoulus [10] investigated the use of a loudness model for the suppression of the late
reverberation in single-channel recordings. An estimate of the direct signal is computed
from the reverberant input signal using a spectral subtraction method, and a reverberation
masking index is derived by means of a computational auditory masking model, which
controls the reverberation processing.
It is a feature of a multi-channel synthesizers and other devices to add reverberation in
order to make the sound better from a perceptual point of view. On the other hand, the
generated reverberation is an artificial signal which when added to the signal at to low
level is barely audible and when added at to high level leads to unnatural and unpleasant
sounding final mixed signal. What makes things even worse is that, as discussed in the
context of Fig. 4a and 4b that the perceived level of reverberation is strongly signaldependent
and, therefore, a certain reverberation filter might work very well for one kind
of signals, but may have no audible effect or, even worse, can generate serious audible
artifacts for a different kind of signals.
An additional problem related to reverberation is that the reverberated signal is intended
for the ear of an entity or individual, such as a human being and the final goal of
generating a mix signal having a direct signal component and a reverberation signal
component is that the entity perceives this mixed signal or "reverberated signal" as
sounding well or as sounding natural. However, the auditory perception mechanism or the
mechanism how sound is actually perceived by an individual is strongly non-linear, not
only with respect to the bands in which the human hearing works, but also with respect to
the processing of signals within the bands. Additionally, it is known that the human
perception of sound is not so much directed by the sound pressure level which can be
calculated by, for example, squaring digital samples, but the perception is more controlled
by a sense of loudness. Additionally, for mixed signals, which include a direct component
and a reverberation signal component, the sensation of the loudness of the reverberation
component depends not only on the kind of direct signal component, but also on the level
or loudness of the direct signal component.
Therefore, there exists a need for determining a measure for a perceived level of
reverberation in a signal consisting of a direct signal component and a reverberation signal
component in order to cope with the above problems related with the auditory perception
mechanism of an entity.
An object of the present invention is, therefore, to provide a apparatus or method for
determining a measure for a perceived level of reverberation or to provide an audio
processor or a method of processing an audio signal with improved characteristics.
This object is achieved by an apparatus for determining a measure for a perceived level of
reverberation in accordance with claim 1, a method of determining a measure for a
perceived level of reverberation i accordance with claim 10, an audio processor in
accordance with claim 11, a method of processing an audio signal i accordance with
claim 14 or a computer program in accordance with claim 15.
The present invention is based on the finding that the measure for a perceived level of
reverberation in a signal is determined by a loudness model processor comprising a
perceptual filter stage for filtering a direct signal component, a reverberation signal
component or a mix signal component using a perceptual filter in order to model an
auditory perception mechanism of an entity. Based on the perceptually filtered signals, a
loudness estimator estimates a first loudness measure using the filtered direct signal and a
second loudness measure using the filtered reverberation signal or the filtered mix signal.
Then, a combiner combines the first measure and the second measure to obtain a measure
for the perceived level of reverberation. Particularly, a way of combining two different
loudness measures preferably by calculating difference provides a quantitative value or a
measure of how strong a sensation of the reverberation is compared to the sensation of the
direct signal or the mix signal.
For calculating the loudness measures, the absolute loudness measures can be used and,
particularly, the absolute loudness measures of the direct signal, the mixed signal or the
reverberation signal. Alternatively, the partial loudness can also be calculated where the
first loudness measure is determined by using the direct signal as the stimulus and the
reverberation signal as noise n the loudness model and the second loudness measure is
calculated by using the reverberation signal as the stimulus and the direct signal as the
noise. Particularly, by combining these two measures in the combiner, a useful measure for
a perceived level of reverberation is obtained. It has been found out by the inventors that
such useful measure cannot be determined alone by generating a single loudness measure,
for example, by using the direct signal alone or the mix signal alone or the reverberation
signal alone. Instead, due to the inter-dependencies in human hearing, combining measures
which are derived differently from either of these three signals, the perceived level of
reverberation in a signal can be determined or modeled with a high degree of accuracy.
Preferably, the loudness model processor provides a time/frequency conversion and
acknowledges the ear transfer function together with the excitation pattern actually
occurring in human hearing an modeled by hearing models.
In a preferred embodiment, the measure for the perceived level of reverberation is
forwarded to a predictor which actually provides the perceived level of reverberation in a
useful scale such as the Sone-scale. This predictor is preferably trained by listening test
data and the predictor parameters for a preferred linear predictor comprise a constant term
and a scaling factor. The constant term preferably depends on the characteristic of the
actually used reverberation filter and, in one embodiment of the reverberation filter
characteristic parameter T6o, which can be given for straightforward well-known
reverberation filters used in artificial reverberators. Even when, however, this
characteristic is not known, for example, when the reverberation signal component is not
separately available, but has been separated from the mix signal before processing in the
inventive apparatus, an estimation for the constant term can be derived.
Subsequently, preferred embodiments of the present invention are described with respect to
the accompanying drawings, in which:
Fig. is a block diagram for an apparatus or method for determining a measure for
a perceived level of reverberation;
is an illustration of a preferred embodiment of the loudness model
processor;
illustrates a further preferred implementation of the loudness model
processor;
illustrates a further preferred implementation of the loudness model
processor;
illustrate examples of time signal envelopes and a corresponding loudness
and partial loudness;
illustrate information on experimental data for training the predictor;
illustrates a block diagram of an artificial reverberation processor;
illustrates three tables for indicating evaluation metrics for embodiments of
the invention;
illustrates an audio signal processor implemented for using the measure for
a perceived level of reverberation for the purpose of artificial reverberation;
illustrates a preferred implementation of the predictor relying on timeaveraged
perceived levels of reverberation; and
illustrates the equations from the Moore Glasberg, Baer publication of 1997
used in a preferred embodiment for calculating the specific loudness.
The perceived level of reverberation depends on both the input audio signal and the
impulse response. Embodiments of the invention aim at quantifying this observation and
predicting the perceived level of late reverberation based o separate signal paths o direct
and reverberant signals, as they appear in digital audio effects. An approach to the problem
is developed and subsequently extended by considering the impact of the reverberation
time on the prediction result. This leads to a linear regression model with two input
variables which is able to predict the perceived level with high accuracy, as shown on
experimental data derived from listening tests. Variations of this model with different
degrees of sophistication and computational complexity are compared regarding their
accuracy. Applications include the control of digital audio effects for automatic mixing of
audio signals.
Embodiments o the present invention are not only useful for predicting the perceived level
of reverberation in speech and music when the direct signal and the reverberation impulse
response (R1R) are separately available. In other embodiments, in which a reverberated
signal occurs, the present invention can be applied as well. In this instance, however, a
direct/ambience or direct/reverberation separator would be included to separate the direct
signal component and the reverberated signal component from the mix signal. Such an
audio processor would then be useful to change the direct/reverberation ratio in this signal
in order to generate a better sounding reverberated signal or better sounding mix signal.
Fig. 1 illustrates a apparatus for determining a measure for a perceived level of
reverberation in a mix signal comprising a direct signal component or dry signal
component 100 and a reverberation signal component 102. The dry signal component 00
and the reverberation signal component 02 are input into a loudness model processor 104.
The loudness model processor is configured for receiving the direct signal component 00
and the reverberation signal component 102 and is furthermore comprising a perceptual
filter stage 04a and a subsequently connected loudness calculator 104b as illustrated in
Fig. 2a. The loudness model processor generates, at its output, a first loudness measure 106
and a second loudness measure 108. Both loudness measures are input into a combiner 110
for combining the first loudness measure 106 and the second loudness measure 108 to
finally obtain a measure 112 for the perceived level of reverberation. Depending on the
implementation, the measure for the perceived level 112 can be input into a predictor 4
for predicting the perceived level of reverberation based on an average value of at least
two measures for the perceived loudness for different signal frames as will be discussed i
the context of Fig. 9. However, the predictor 4 in Fig. 1 is optional and actually
transforms the measure for the perceived level into a certain value range or unit range such
as the Sone-unit range which is useful for giving quantitative values related to loudness.
However, other usages for the measure for the perceived level 112 which is not processed
by the predictor 114 can be used as well, for example, i the audio processor of Fig. 8,
which does not necessarily have to rely on a value output by the predictor 114, but which
can also directly process the measure for the perceived level 12, either in a direct form or
preferably in a kind of a smoothed form where smoothing over time is preferred in order to
not have strongly changing level corrections of the reverberated signal or, as discussed
later on, of the gain factor g illustrated in Fig. 6 or illustrated in Fig. 8.
Particularly, the perceptual filter stage is configured for filtering the direct signal
component, the reverberation signal component or the mix signal component, wherein the
perceptual filter stage is configured for modeling an auditory perception mechanism o an
entity such as a human being to obtain a filtered direct signal, a filtered reverberation
5 signal or a filtered mix signal. Depending on the implementation, the perceptual filter stage
may comprise two filters operating in parallel or can comprise a storage and a single filter
since one and the same filter can actually be used for filtering each of the three signals, i.e.,
the reverberation signal, the mi signal and the direct signal. In this context, however, it is
to be noted that, although Fig. 2a illustrates n filters modeling the auditory perception
10 mechanism, actually two filters will be enough or a single filter filtering two signals out of
the group comprising the reverberation signal component, the mix signal component and
the direct signal component.
The loudness calculator 104b or loudness estimator is configured for estimating the first
, 15 loudness-related measure using the filtered direct signal and for estimating the second
loudness measure using the filtered reverberation signal or the filtered mix signal, where
the mix signal is derived fro a super position of the direct signal component and the
reverberation signal component.
20 Fig. 2c illustrates four preferred modes of calculating the measure for the perceived level
of reverberation. Embodiment 1 relies on the partial loudness where both, the direct signal
component x and the reverberation signal component r are used in the loudness model
processor, but where, in order to determine the first measure ESTl, the reverberation signal
is used as the stimulus and the direct signal is used as the noise. For determining the
25 second loudness measure EST2, the situation is changed, and the direct signal component
is used as a stimulus and the reverberation signal component is used as the noise. Then, the
measure for the perceived level of correction generated by the combiner is a difference
between the first loudness measure ESTl and the second loudness measure EST2.
, 30 However, other computationally efficient embodiments additionally exist which are
indicated at lines 2, 3, and 4 in Fig. 2c. These more computationally efficient measures rely
on calculating the total loudness of three signals comprising the mix signal m, the direct
signal and the reverberation signal n. Depending on the required calculation performed
by the combiner indicated in the last column of Fig. 2c, the first loudness measure ESTl is
35 the total loudness of the mix signal or the reverberation signal and the second loudness
measure EST2 is the total loudness of the direct signal component x or the mix signal
component m, where the actual combinations are as illustrated i Fig. 2c.
In a further embodiment, the loudness model processor 104 is operating in the frequency
domain as discussed i more detail in Fig. 3. in such a situation, the loudness model
processor and, particularly, the loudness calculator 04b provides a first measure and a
second measure for each band. These first measures over all bands are subsequently
added or combined together in an adder 104c for the first branch and 104d for the second
branch in order to finally obtain a first measure for the broadband signal an a second
measure for the broadband signal.
Fig. 3 illustrates the preferred embodiment of the loudness model processor which has
already been discussed in some aspects with respect to the Figs. 1, 2a, 2b, 2c. Particularly,
the perceptual filter stage 104a comprises a time-frequency converter 300 for each branch,
where, in the Fig. 3 embodiment, x[k] indicates the stimulus and n[k] indicates the noise.
The time/frequency converted signal is forwarded into an ear transfer function block 302
(Please note that the ear transfer function can alternatively be computed prior to the timefrequency
converter with similar results, but higher computational load) and the output of
this block 302 is input into a compute excitation pattern block 304 followed by a temporal
integration block 306. Then, in block 308, the specific loudness in this embodiment is
calculated, where block 308 corresponds to the loudness calculator block 104b in Fig. 2a.
Subsequently, a integration over frequency in block 310 is performed, where block 310
corresponds to the adder already described as 04c and 104d i Fig. 2b. It is to be noted
that block 310 generates the first measure for a first set of stimulus and noise and the
second measure for a second set of stimulus and noise. Particularly, when Fig. 2b is
considered, the stimulus for calculating the first measure is the reverberation signal and the
noise is the direct signal while, for calculating the second measure, the situation is changed
and the stimulus is the direct signal component and the noise is the reverberation signal
component. Hence, for generating two different loudness measures, the procedure
illustrated in Fig. 3 has been performed twice. However, changes in the calculation only
occur in block 308 which operates differently as discussed furthermore in the context of
Fig. 10, so th t the steps illustrated by blocks 300 to 306 only have to be performed once,
and the result of the temporal integration block 306 can be stored in order to compute the
first estimated loudness and the second estimated loudness for embodiment 1 in Fig. 2c. It
is to be noted that, for the other embodiments 2, 3, 4 in Fig. 3c, block 308 s replaced by an
individual block "compute total loudness" for each branch, where, in this embodiment it is
indifferent, whether one signal is considered to be a stimulus or a noise.
Subsequently, the loudness model illustrated in Fig. 3 is discussed in more detail.
The implementation of the loudness model in Fig. 3 follows the descriptions in [ 1 1, 12]
with modifications as detailed later on. The training and the validation of the prediction
uses data from listening tests described in [13 and briefly summarized later. The
application of the loudness model for predicting the perceived level of late reverberation is
described later on as well. Experimental results follow.
This section describes the implementation of a model of partial loudness, the listening test
data that was used as ground truth for the computational prediction of the perceived level
of reverberation, and a proposed prediction method which is based on the partial loudness
model.
The loudness model computes the partial loudness N, „[k] of a signal x [k] when
presented simultaneously with a masking signal n[k]
Although early models have dealt with the perception of loudness in steady background
noise, some work exists on loudness perception in backgrounds of co-modulated random
noise [14], complex environmental sounds [12], and music signals [15]. Fig. 4b illustrates
the total loudness and the partial loudness of its components of the example signal shown
in Fig. 4a, computed with the loudness model used here.
The model used in this work is similar to the models in [ 1, 12] which itself drew on
earlier research by Fletcher, Munson, Stevens, and Zwicker, with some modifications as
described in the following. A block diagram of the loudness model is shown in Fig. 3. The
input signals are processed in the frequency domain using a Short- i e Fourier transform
(STFT). In [12], 6 DFTs of different lengths are used in order to obtain a good match for
the frequency resolution and the temporal resolution to that of the human auditor}' system
at all frequencies. In this work, only one DFT length is used for the sake of computational
efficiency, with a frame length of 2 ms at a sampling rate of 48 kHz, 50% overlap and a
Hann window function. The transfer through the outer and middle ear is simulated with a
fixed filter. The excitation function is computed for 40 auditory filter bands spaced on the
equivalent rectangular bandwidth (ERB) scale using a level dependent excitation pattern.
In addition to the temporal integration due to the windowing of the STFT, a recursive
integration is implemented with a time constant of 25 ms, which is only active at times
where the excitation signal decays.
The specific partial loudness, i.e., the partial loudness evoked in each of the auditory filter
band, is computed from the excitation levels from the signal of interest (the stimulus) and
the interfering noise according to Equations ( 17)-(20) in [11 , illustrated in Fig. 10. These
equations cover the four cases where the signal is above the hearing threshold in noise or
not, and where the excitation of the mixture signal is less than 00 B or not. If no
interfering signal is fed into the model, i.e. [ ] =- 0 , the result equals the total loudness
Nx [k] of the stimulus x [k .
Particularly, Fig. 1 illustrates equations 17, 18, 19, 20 of the publication " A Model for
the Prediction of Thresholds, Loudness and Partial Loudness", B.C.J. Moore, B.R.
Glasberg, T. Baer, J . Audio Eng. Soc, Vol. 45, No. 4 , April 1997. This reference describes
the case of a signal presented together with a background sound. Although the background
may be any type of sound, it is referred to as "noise" in this reference to distinguish it from
the signal whose loudness is to be judged. The presence of the noise reduces the loudness
of the signal, an effect called partial masking. The loudness f the signal grows very
rapidly when its level is increased from a threshold value to a value 20-3 OdB above
threshold. I the paper it is assumed that the partial loudness of a signal presented in noise
can be calculated by summing the partial specific loudness of the signal across frequency
(on an ERB -scaie). Equations are derived for calculating the partial specific loudness by
considering four limiting cases. ESIG denotes the excitation evoked b the signal and NOISE
denotes the excitation evoked by the noise. It is assumed that ESIG>ETHRQ and ESIG plus
ENOISE< 10 . The total specific loudness '' OT is defined as follows:
N TOT = C{[(E s + E i )G + A - A }
It is assumed that the listener can partition a specific loudness at a given center frequency
between the specific loudness of the signal and that of the noise, but in a way that prefers
the total specific loudness.
This assumption is consistent, since i most experiments measuring partial masking, the
listener hears first the noise alone and then the noise plus signal. The specific loudness for
the noise alone, assuming that it is above threshold, is
1-
Hence, if the specific loudness of the signal were derived simply by subjecting the specific
loudness o f the noise from the total specific loudness, the result would be
NsiG = C { [ Es G + E )G + A - A - C[(E S G + A) A ]
In practice, the way that specific loudness is partitioned between signal and noise appears
to vary depending on the relative excitation of the signal and the noise.
Four situations are considered that indicate how specific loudness is assigned at different
signal levels. Let .Z T R denote the peak excitation evoked by a sinusoidal signal when it is
a t its masked threshold in the background noise. When J¾IG is well below T all the
specific loudness is assigned to the noise, and the partial specific loudness of the signal
approaches zero. Second, when -ENOISE is well below 'THRQ the partial specific loudness
approaches the value it would have for a signal i quiet. Third, when the signal is at its
masked threshold, with excitation E it is assumed that the partial specific loudness is
equal to the value that would occur for a signal a t the absolute threshold. Finally, when a
signal is centered in narrow-band noise is well above its masked threshold, the loudness of
the signal approaches its unmasked value. Therefore, the partial specific loudness o f the
signal also approaches its unmasked value.
Consider the implications of these various boundary conditions. At masked threshold, the
specific loudness equal that for a signal at threshold i quiet. This specific loudness is less
than it would be predicted from the above equation, presumably because some of the
specific loudness of the signal is assigned to the noise. In order to obtain the correct
specific loudness for the signal, it is assumed that the specific loudness assigned to the
noise is increased b y the factor B, where
+ + ~ ( m + A )
EG +
~
Applying this factor to the second term in the above equation for gives
N G = C (E + E i p + A - A''} -C{[(E^
It is assumed that when the signal is at masked threshold, its peak excitation ¾ - is equal
to ¾ o SE+ Q where K is the signal-to-noise ratio at the output of the auditory filter
required for threshold at higher masker levels. Recent estimates o f K, obtained for masking
experiments using notched noise, suggest that K increases markedly at very low
frequencies, becoming greater than unity. In the reference, the value of K is estimated as a
function of frequency. The value decreases from high levels at low frequencies to constant
low levels at higher frequencies. Unfortunately, there are no estimates for K for center
frequencies below 100 Hz, s o values from 50 to 100 Hz substituting ETHRN the above
equation results in:
NSIG = C{[(E S1G + E 0 S )G + A)" - A } - C{r(£ NO (l + K) + E Q)G + A]" - E G + A)"}
When EsiG =E iRN this equation specifics the peak specific loudness for a signal at the
absolute threshold in quiet.
When the signal i s well above its masked threshold, that is, when si £™ the
specific loudness of the signal approaches the value that it would have when no
background noise is present. This means that the specific loudness assigned to the noise
becomes vanishingly small. To accommodate this, the above equation is modified by
introducing an extra term which depends on the ratio E HR / Esio- This term decreases as E
EsiG i s increased above the value corresponding to masked threshold. Hence, the above
equation becomes equation 17 on Fig. 10 .
This is the final equation for N ' in the case when EsiG ETHRN d ES G+E 0 SE< 10 10 . The
exponent 0.3 in the final term was chosen empirically so as to give a good fit to data on the
loudness of a tone in noise as a function of the signal-to-noise ratio.
Subsequently, the situation is considered where Es G ETHRN- the limiting case where
ESIG is just below ETHRN, the specific loudness would approach the value given in Equation
17 in Fig. 10. When EsiG is decreased to a value well below E HR the specific loudness
should rapidly become very small. This is achieved by Equation 8 in Fig. 10. The first
term in parenthesis determines the rate at which a specific loudness decreases as Es is
decreased below ETHRN- This describes the relationship between specific loudness and
excitation for a signal i quiet when EsiG <-ETHRQ except that ET has been substituted i
Equation 18. The first term in braces ensures that the specific loudness approaches the
value defined by Equation 17 of Fig. 0 as E iG approaches ETHRNThe
equations for partial loudness described so far apply when EsiG ÷ Ois < 0 . By
applying the same reasoning as used for the derivation of equation (17) of Fig. 10, any
equation can be derived for the case E OisE³ET R and as outlined in
equation 19 in Fig. 10. C2=C/( 1.04xl0 6)0 5. Similarly, by applying the same reasoning as
used for the derivation of equation (18) of Fig. 0, an equation can be derived for the case
where Esi ETH and EsiG+ENOisE> 10 as outlined in equation 20 in Fig. 10.
The following points are to be noted. This prior art model is applied for the present
invention where, in a first run, SIG corresponds to for example, the direct signal as the
"stimulus" and Noise corresponds to for example the reverberation signal or the mix signal
as the "noise". In the second run as discussed in the context of the first embodiment in Fig.
2c, SIG would then correspond to the reverberation signal as the "stimulus" and "noise"
would correspond to the direct signal. Then, the two loudness measures are obtained which
are then combined by the combiner preferably by forming a difference.
In order to assess the suitability of the described loudness model for the task of predicting
the perceived level of the late reverberation, a corpus of ground truth generated from
listener responses is preferred. To this end, data from an investigation featuring several
listening test [13] is used in this paper which is briefly summarized in the following. Each
listening test consisted of multiple graphical user interface screens which presented
mixtures of different direct signals with different conditions of artificial reverberation. The
listeners were asked to rate this perceived amount of reverberation on a scale from 0 to 100
points. In addition, two anchor signals were presented at 0 points and at 90 points. The
listeners were asked to rate the perceived amount of reverberation on a scale from 0 to 100
points. In addition, two anchor signals were presented at 10 points and at 90 points. The
anchor signals were created from the same direct signal with different conditions of
reverberation.
The direct signals used for creating the test items were monophonic recordings of speech,
individual instruments and music of different genres with a length of about 4 seconds each.
The majority of the items originated from anechoic recordings but also commercial
recordings with a small amount of original reverberation were used.
The RIRs represent late reverberation and were generated using exponentially decaying
white noise with frequency dependent decay rates. The decay rates are chosen such that the
reverberation time decreases from low to high frequencies, starting at a base reverberation
time T . Early reflections were neglected in this work. The reverberation signal r [k and
the direct signal x[k] were scaled and added such that the ratio of their average loudness
measure according to ITU-R BS.1770 [16] matches a desired DRR and such that all test
signal mixtures have equal long-term loudness. All participants in the tests were working
in the field of audio and had experience with subjective listening tests.
The ground truth data used for the training and the verification / testing of the prediction
method were taken fro two listening tests and are denoted by A and B , respectively.
The data set A consisted of ratings of 14 listeners for 54 signals. The listeners repeated the
test once and the mean rating was obtained from all of the 28 ratings for each item. The 54
signals were generated by combining 6 different direct signals and 9 stereophonic
reverberation conditions, with T e {1,1.6,2.4} s and DRR e {3,7.5,12} dB, and no predelay.
The data in B were obtained from ratings of 14 listeners for 60 signals. The signals were
generated using 15 direct signals and 36 reverberation conditions. The reverberation
conditions sampled four parameters, namely T¥ , DRR, pre-delay, and ICC. For each direct
signal 4 RIRs were chosen such that two had no pre-delay and two had a short pre-delay of
50 ms, and two were monophonic and two were stereophonic.
Subsequently, further features of a preferred embodiment of the combiner 10 in Fig. 1 are
discussed.
The basic input feature for the prediction method is computed from the difference of the
partial loudness Nr x [k] of the reverberation signal r[k] (with the direct signal x[k]
being the interferer) and the loudness NX [k] of x[k] (where r[k] is the interferer),
according to Equation 2.
r x [k] N [k]-N x [k]
The rationale behind Equation (2) is that the difference AN _[k] is a measure of how
strong the sensation of the reverberation is compared to the sensation of the direct signal.
Taking the difference was also found to make the prediction result approximately invariant
with respect to the playback level. The playback level has an impact on the investigated
sensation 17, 8], but to a more subtle extent than reflected by the increase of the partial
loudness Nr x with increasing playback level. Typically, musical recordings sound more
reverberant at moderate to high levels (starting at about 75-80 dB SPL) than at about 12 to
20 dB lower levels. This effect is especially obvious in cases where the DR is positive,
which is valid "for nearly all recorded music" [18], but not in all cases for concert music
where "listeners are often well beyond the critical distance" [6].
The decrease of the perceived level of the reverberation with decreasing playback level is
best explained by the fact that the dynamic range of reverberation is smaller than that of
the direct sounds (or, a time-frequency representation of reverberation is more dense
whereas a time-frequency representation of direct sounds is more sparse [19]). In such a
scenario, the reverberation signal is more likely to fall below the threshold of hearing than
the direct sounds do.
Although equation (2) describes, as the combination operation, a difference between the
two loudness measures Nr X[k] and Nx [ ] , other combinations can be performed as well
such as multiplications, divisions or even additions. In any case, it is sufficient that the two
alternatives indicated by the two loudness measures are combined i order to have
influences of both alternatives in the result. However, the experiments have shown that the
difference results in the best values from the model, i.e. in the results o the model which
fit with the listening tests to a good extent, so that the difference is the preferred way of
combining.
Subsequently, details of the predictor 1 4 illustrated in Fig. 1 are described, where these
details refer to a preferred embodiment.
The prediction methods described in the following are linear and use a least squares fit for
the computation of the model coefficients. The simple structure of the predictor is
advantageous in situations where the size of the data sets for training and testing the
predictor is limited, which could lead to overfitting of the model when using regression
methods with more degrees of freedom, e.g. neural networks. The baseline predictor Rb is
derived by the linear regression according to Equation (3) with coefficients ai , with K
being the length of the signal in frames,
The model has only one independent variable, i.e. the mea of D [k] . To track changes
and to be able to implement a real-time processing, the computation of the mean can be
approximated using a leaky integrator. The model parameters derived when using data set
A for the training are a - 48.2 and a, = 14.0 , where 0 equals the mean rating for all
listeners and items.
Fig. 5a depicts the predicted sensations for data set A . It can be seen that the predictions
are moderately correlated with the mean listener ratings with a correlation coefficient of
0.71 . Please note that the choice o the regression coefficients does not affect this
correlation. As shown in the lower plot, for each mixture generated by the same direct
signals, the points exhibit a characteristic shape centered close to the diagonal. This shape
indicates that although the baseline model R is able to predict R to some degree, it does
not reflect the influence of 0 on the ratings. The visual inspection of the data points
suggests a linear dependency on T6 . If the value of T is known, as is the case when
controlling an audio effect, it can be easily incorporated into the linear regression model to
derive a enhanced prediction
The model parameters derived from the data set A are a0 = 48.2 , a = 12.9 , a = 10.2 .
The results are shown in Fig. 5b separately for each of the data sets. The evaluation of the
results is described in more detail in the next section.
Alternatively, an averaging over more or less blocks can be performed as long as an
averaging over at least two blocks takes place, although, due to the theory of linear
equation, the best results may be obtained, when an averaging over the whole music piece
up to a certain frame is performed. However, for real time applications, it is preferred to
reduce the number of frames over which is averaged depending on the actual application.
Fig. 9 additionally illustrates that the constant term is defined by a and a -T o. The second
term a2-T60 has been selected in order to be in the position to apply this equation not only
to a single reverberator, i.e., to a situation in which the filter 600 of Fig. 6 is not changed.
This equation which, of course, is a constant term, but which depends on the actually used
reverberation filters 606 of Fig. 6 provides, therefore, the flexibility to use exactly the same
equation for other reverberation filters having other values of T o. As known in the art, T o
is a parameter describing a certain reverberation filter and, particularly means that the
reverberation energy has been decreased by 60dB from an initial maximum reverberation
energy value. Typically, reverberation curves are decreasing with time and, therefore, T 0
indicates a time period, n which a reverberation energy generated by a signal excitation
has decreased by 60dB. Similar results in terms of prediction accuracy are obtained by
replacing T 0 by parameters representing similar information (that of the length of the
MR), e.g. T30 ·
In the following, the models are evaluated using the correlation coefficient , the mean
absolute error (MAE) and the root mean squared error (RMSE) between the mean listener
ratings and the predicted sensation. The experiments are performed as two-fold crossvalidation,
i.e. the predictor is trained with data set A and tested with data set B, and the
experiment is repeated with B for training and A for testing. The evaluation metrics
obtained from both runs are averaged, separately for the training and the testing.
The results are shown in Table 1 for the prediction models R and Re . The predictor
R yields accurate results with an RMSE of 10.6 points. The average of the standard
deviation of the individual listener ratings per item are given as a measure for the
dispersion from the mean (of the ratings of all listeners per item) as s A = 13.4 for data set
A and = 13.6 for data set B . The comparison to the RMSE indicates that R is at
least as accurate as the average listener in the listening test.
The accuracies of the predictions for the data sets differ slightly, e.g. for Re both MAE
and RMSE are approximately one point below the mean value (as listed in the table) when
testing with data set A and one point above average when testing with data set B . The fact
that the evaluation metrics for training and test are comparable indicates that overfitting of
the predictor has been avoided.
In order to facilitate an economic implementation of such prediction models, the following
experiments investigate how the use of loudness features with less computational
complexity influence the precision of the prediction result. The experiments focus on
replacing the partial loudness computation by estimates of total loudness and on simplified
implementations of the excitation pattern.
Instead of using the partial loudness difference N
J
[k , three differences of total
loudness estimates are examined, with the loudness of the direct signal Nx [k , the
loudness of the reverberation Nr [k] , and the loudness of the mixture signal Nm[k , as
shown in Equations (5)-(7), respectively.
. ( )
Equation (5) is based on the assumption that the perceived level of the reverberation signal
can be expressed as the difference (increase) in overall loudness which is caused by adding
the reverb to the dry signal .
Following a similar rationale as for the partial loudness difference in Equation (2),
loudness features using the differences of total loudness o the reverberation signal and the
mixture signal or the direct signal, respectively, are defined in Equations (6) and (7). The
measure for predicting the sensation is derived from as the loudness of the reverberation
signal when listened to separately, with subtractive terms for modelling the partial masking
and for normalization with respect to playback level derived from the mixture signal or the
direct signal, respectively.
ANr__m[k]=N [k] - Nm[k] ( 6 )
N [k] =Nr[k N [k] ( 7 )
Table 2 shows the results obtained with the features based on the total loudness and reveals
that i fact two of them, ANmx[k] and N [k] , yield predictions with nearly the same
accuracy as R. But as shown in Table 2, even A r [k ] provides use for results.
Finally, in an additional experiment, the influence of the implementation of the spreading
function is investigated. This is of particular significance for many application scenarios,
because the use of the level dependent excitation patterns demands implementations of
R high computational complexity. The experiments with a similar processing as for e but
using one loudness model without spreading and one loudness model with level-invariant
spreading function led to the results shown in Table 2. The influence of the spreading
seems to be negligible.
Therefore, equations (5), (6) and (7) which indicate embodiments 2, 3, 4 of Fig. 2c
illustrate that even without partial loudnesses, but with total loudnesses, for different
combinations of signal components or signals, good values or measures for the perceived
level of reverberation in a mi signal are obtained as well.
Subsequently, a preferred application of the inventive determination of measures for a
perceived level of reverberation are discussed in the context of Fig. 8. Fig. 8 illustrates an
audio processor for generating a reverberated signal from a direct signal component input
at an input 800. The direct or dry signal component is input into a reverberator 801, which
can be similar to the reverberator 606 in Fig. 6. The dry signal component of input 800 is
additionally input into an apparatus 802 for determining the measure for a perceived
loudness which can be implemented as discussed i the context of Fig. 1, Fig. 2a and 2c, 3,
9 and 10. The output of the apparatus 802 is the measure R for a perceived level f
reverberation i a mix signal which is input into a controller 803. The controller 803
receives, at a further input, a target value for the measure of the perceived level of
reverberation and calculates, from this target value and the actual value R again a value on
output 804.
This gain value is input into a manipulator 805 which is configured for manipulating, in
this embodiment, the reverberation signal component 806 output by the reverberator 801.
As illustrated Fig. 8, the apparatus 802 additionally receives the reverberation signal
component 806 as discussed in the context of Fig. 1 and the other Figs, describing the
apparatus for determining a measure of a perceived loudness. The output of the
manipulator 805 is input into an adder 807, where the output of the manipulator comprises
in the Fig. 8 embodiment the manipulated reverberation component and the output of the
adder 807 indicates a mix signal 808 with a perceived reverberation as determined by the
target value. The controller 803 can be configured to implement any of the control rules as
defined in the art for feedback controls where the target value is a set value and the value R
generated by the apparatus is an actual value and the gain 804 is selected so that the actual
value R approaches the target value input into the controller 803. Although Fig. 8 is
illustrated in that the reverberation signal is manipulated by the gain in the manipulator 805
which particularly comprises a multiplier or weighter, other implementations can be
performed as well. One other implementation, for example, is that not the reverberation
signal 806 but the dry signal component is manipulated by the manipulator as indicated by
optional line 809. In this case, the non-manipulated reverberation signal component as
output by the reverberator 801 would be input into the adder 807 as illustrated by optional
line 810. Naturally, even a manipulation of the dry signal component and the reverberation
signal component could be performed in order to introduce or set a certain measure of
perceived loudness of the reverberation in the mix signal 808 output by the adder 807. One
other implementation, for example, is that the reverberation time T o is manipulated.
The present invention provides a simple and robust prediction of the perceived level of
reverberation and, specifically, late reverberation in speech and music using loudness
models o varying computational complexity. The prediction modules have been trained
and evaluated using subjective data derived from three listening tests. As a starting point,
the use of a partial loudness model has lead to a prediction model with high accuracy when
the T6oof the RIR 606 of Fig. 6 is known. This result is also interesting from the perceptual
point of view, when it is considered that the model of partial loudness was not originally
developed with stimuli of direct and reverberant sound as discussed in the context of Fig.
10. Subsequent modifications of the computation of the input features for the prediction
method leads to a series of simplified models which were shown to achieve comparable
performance for the data sets at hand. These modifications included the use of total
loudness models and simplified spreading functions. The embodiments of the present
invention are also applicable for more diverse R Rs including early reflections and larger
pre-delays. The present invention is also useful for determining and controlling the
perceived loudness contribution of other types of additive or reverberant audio effects.
Although some aspects have been described in the context of an apparatus, i is clear that
these aspects also represent a description of the corresponding method, where a block or
device corresponds to a method step or a feature of a method step. Analogously, aspects
described in the context of a method step also represent a description of a corresponding
block or item or feature of a corresponding apparatus.
Depending on certain implementation requirements, embodiments of the invention can be
implemented in hardware or in software. The implementation can be performed using a
digital storage medium, for example a floppy disk, a DVD, a CD, a ROM, a PROM, an
EPROM, an EEPROM or a FLASH memory, having electronically readable control
signals stored thereon, which cooperate (or are capable of cooperating) with a
programmable computer system such that the respective method is performed.
Some embodiments according to the invention comprise a non-transitory or tangible data
carrier having electronically readable control signals, which are capable of cooperating
with a programmable computer system, such that one of the methods described herein is
performed.
Generally, embodiments of the present invention can be implemented as a computer
program product with a program code, the program code being operative for performing
one of the methods when the computer program product runs o a computer. The program
code may for example be stored on a machine readable carrier.
Other embodiments comprise the computer program for performing one of the methods
described herein, stored on a machine readable carrier.
In other words, a embodiment of the inventive method is, therefore, a computer program
having a program code for performing one of the methods described herein, when the
computer program runs on a computer.
A further embodiment of the inventive methods is, therefore, a data carrier (or a digital
storage medium, or a computer-readable medium) comprising, recorded thereon, the
computer program for performing one of the methods described herein.
A further embodiment of the inventive method is, therefore, a data stream or a sequence of
signals representing the computer program for performing one o the methods described
herein. The data stream or the sequence of signals may for example be configured to be
transferred via a data communication connection, for example via the Internet.
A further embodiment comprises a processing means, for example a computer, or a
programmable logic device, configured to or adapted to perform one the methods
described herein.
A further embodiment comprises a computer having installed thereon the computer
program for performing one of the methods described herein.
I some embodiments, a programmable logic device (for example a field programmable
gate array) may be used to perform some or all of the functionalities of the methods
described herein. In some embodiments, a field programmable gate array may cooperate
with a microprocessor in order to perform one of the methods described herein. Generally,
the methods are preferably performed by any hardware apparatus.
The above described embodiments are merely illustrative for the principles of the present
invention. It is understood that modifications and variations of the arrangements and the
details described herein will be apparent to others skilled in the art. It is the intent,
therefore, to be limited only by the scope of the impending patent claims and not by the
specific details presented by way of description and explanation of the embodiments
herein.
List of References
[1] A. Czyzewski, "A method for artificial reverberation quality testing," J. Audio Eng.
Soc, vol. 38, pp. 129-141, 1990.
[2] J .A. Moorer, "About this reverberation business," Computer Music Journal, vol. 3,
1979.
[3] B. Scharf, "Fundamentals of auditory masking," Audiology, vol. 10, pp. 30-40, 1971.
[4] W.G. Gardner and D. Griesinger, "Reverberation level matching experiments," in Proc.
of the Sabine Centennial Symposium, Aconst. Soc. of Am., 1994.
[5] D. Griesinger, "How loud is my reverberation," in Proc. Of the AES 98th Conv., 1995.
[6] D. Griesinger, "Further investigation into the loudness of running reverberation," in
Proc. of the Institute of Acoustics (UK) Conference, 1995.
[7] D. Lee and D. Cabrera, "Effect of listening level and background noise on the
subjective decay rate of room impulse responses: Using time varying-loudness to model
reverberance," Applied Acoustics, vol. 71, pp. 801-81 1, 2010.
[8] D. Lee, D. Cabrera, and W.L. Martens, "Equal reverberance matching of music," Proc.
of Acoustics, 2009.
[9] D. Lee, D. Cabrera, and W.L. Martens, "Equal reverberance matching of running
musical stimuli having various reverberation times and SPLs," in Proc. of the 20th
International Congress on Acoustics, 2010.
[10] A. Tsilfidis and J . Mourjopoulus, "Blind single-channel suppression of late
reverberation based on perceptual reverberation modeling," J. Aconst. Soc. Am, vol. 129,
pp. 1439-1451, 201 1.
[ 1 ] B.C.J. Moore, B.R. Glasberg, and T. Baer, "A model for the prediction of threshold,
loudness, and partial loudness," J. Audio Eng. Soc. , vol. 45, pp. 224-240, 1997.
[12] B.R. Glasberg and B.C.J. Moore, "Development and evaluation of a model for
predicting the audibility of time varying sounds in the presence of the background sounds,"
. Audio Eng. Soc, vol. 53, pp. 906-918, 2005.
[13] J. Paulus, C. Uhle, and J . Herre, "Perceived level of late reverberation in speech and
music," in Proc. of the AES 130 h Conv. , 201 1.
[14] J.L. Verhey and S.J. Heise, "Einfluss der Zeitstruktur dcs Hintergrundes au die
Tonhaltigkeit und Lautheit des tonalen Vordergrundes (in German)," in Proc. of DAGA,
2010.
[15] C. Bradter and K. Hobohm, "Loudness calculation for individual acoustical objects
within complex temporally variable sounds," in Proc. of the AES 124th Co ., 2008.
[16] International Telecommunication Union, Radiocommunication Assembly,
"Algorithms to measure audio programme loudness and true-peak audio level,"
Recommendation ITU-R BS. 1770, 2006, Geneva, Switzerland.
[17] S. Hase, A. Takatsu, S. Sato, H. Sakai, and Y. Ando, "Reverberance of an existing
hall in relation to both subsequent reverberation time and SPL," J Sound Vib., vol. 232,
pp. 149-155, 2000.
[18] D. Griesinger, "The importance of the direct to reverberant ratio in the perception of
distance, localization, clarity, and envelopment," in Proc. of the AES 126th Conv. , 2009.
[19] C. Uhle, A. Walther, O. Hellmuth, and J . Herre, "Ambience separation fro mono
recordings using Non-negative Matrix Factorization," in Proc. of the AES 30 h Conf, 2007.
Claims
1. Apparatus for determining a measure for a perceived level of reverberation in a mix
signal consisting of a direct signal component (100) and a reverberation signal
component (102), comprising:
a loudness model processor (104) comprising a perceptual filter stage for filtering
the dry signal component (100), the reverberation signal component ( 102) or the
mi signal, wherein the perceptual filter stage is configured for modeling an
auditory perception mechanism of an entity to obtain a filtered direct signal, a
filtered reverberation signal or a filtered mix signal;
a loudness estimator for estimating a first loudness measure using the filtered direct
signal and for estimating a second loudness measure using the filtered reverberation
signal or the filtered mix signal, where the filtered mix signal is derived from a
superposition of the direct signal component and the reverberation signal
component; and
a combiner ( 110) for combining the first and the second loudness measures (106,
08) to obtain a measure ( 112) for the perceived level of reverberation.
2. Apparatus in accordance with claim 1, in which the loudness estimator (1 4b) is
configured to estimate the first loudness measure so that the filtered direct signal is
considered to be a stimulus and the filtered reverberation signal is considered to be
a noise, or to estimate the second loudness measure (108) so that the filtered
reverberation signal is considered to be a stimulus and the filtered direct signal is
considered to be a noise.
3. Apparatus in accordance with claim 1 or 2, in which the loudness estimator (104b)
is configured to calculate the first loudness measure as a loudness of the filtered
direct signal or to calculate the second loudness measure as a loudness of the
filtered reverberation signal or the mix signal.
4. Apparatus in accordance with one of the preceding claims, n which the combiner
( 110) is configured to calculate a difference using the first loudness measure (106)
and the second loudness measure (108).
5. Apparatus in accordance with claim 1, further comprising:
a predictor ( 1 14) for predicting the perceived level of reverberation based on a
average value (904) f a least two measures for the perceived loudness for
different signal frames (k).
6. Apparatus in accordance with claim 5, in which the predictor ( 114) is configured to
use, in a prediction (900) a constant term (901, 903), a linear term depending on the
average value (904) and a scaling factor (902).
7 Apparatus in accordance with claim 5 or 6, in which the constant term (903)
depends o the reverberation parameter describing the reverberation filter (606)
used for generating the reverberation signal in an artificial reverberator.
8. Apparatus in accordance with one of the preceding claims, i which the filter stage
comprises a time- frequency conversion stage (300),
wherein the loudness estimator (104b) is configured to su (104c, 104d) results
obtained for a plurality of bands to derive the first and the second loudness
measures (106, 108) for a broadband mix signal comprising the direct signal
component and the reverberation signal component.
9. Apparatus in accordance with one of the preceding claims, in which the filter stage
(104a) comprises:
an ear transfer filter (302), a excitation pattern calculator (304), and a temporal
integrator (306) to derive the filtered direct signal or the filtered reverberation
signal or the filtered mix signal.
10. Method of determining a measure for a perceived level of reverberation in a mix
signal consisting of a direct signal component (100) an a reverberation signal
component (102), comprising:
filtering (104) the dry signal component (100), the reverberation signal component
(102) or the mix signal, wherein the filtering is performed using a perceptual filter
stage being configured for modeling an auditory perception mechanism o an entity
to obtain a filtered direct signal, a filtered reverberation signal or a filtered mix
signal;
estimating a first loudness measure using the filtered direct signal;
estimating a second loudness measure using the filtered reverberation signal or the
filtered mix signal, where the filtered mix signal is derived from a superposition of
the direct signal component and the reverberation signal component; and
combining ( 10) the first and the second loudness measures (106, 108) to obtain a
measure ( 12) for the perceived level of reverberation.
11. Audio processor for generating a reverberated signal (808) from a direct signal
component (800), comprising:
a reverberator (801) for reverberating the direct signal component (800) to obtain a
reverberated signal component (806);
an apparatus for determining a measure for a perceived level of reverberation in the
reverberated signal comprising the direct signal component and the reverberated
signal component in accordance with one of the claims 1 to 9;
a controller (803) for receiving the perceived level (R) generated by the apparatus
(802) for determining a measure of a perceived level of reverberation, and for
generating a control signal (804) in accordance with the perceived level and a target
value;
a manipulator (805) for manipulating the dry signal component (800) or the
reverberation signal component (806) in accordance with the control value (804);
and
a combiner (807) for combining the manipulated dry signal component and the
manipulated reverberation signal component, or for combining the dry signal
component and the manipulated reverberation signal component, or for combining
the manipulated dry signal component and the reverberation signal component to
obtain the mix signal (808).
12. Apparatus in accordance with claim , in which the manipulator (805) comprises a
weighter for weighting the reverberation signal component by a gain value, the gain
value being determined by the control signal, or
in which the reverberator (801) comprises a variable filter, the filter being variable
in response to the control signal (804).
Apparatus i accordance with claim , in which the reverberator (801) has a fixed
filter,
in which the manipulator (805) has the weighter to generate the manipulated
reverberation signal component, and
in which the adder (807) is configured for adding the direct signal component an
the manipulated reverberation signal component to obtain the mixed signal (808).
Method of processing an audio signal for generating a reverberated signal (808)
from a direct signal component (800), comprising:
reverberating (801) the direct signal component (800) to obtain a reverberated
signal component (806);
a method of determining a measure for a perceived level of reverberation in the
reverberated signal comprising the direct signal component and the reverberated
signal component in accordance with claim 10;
receiving the perceived level (R) generated by the method (802) for determining a
measure of a perceived level of reverberation,
generating (803) a control signal (804) in accordance with the perceived level and a
target value;
manipulating (805) the dry signal component (800) or the reverberation signal
component (806) in accordance with the control value (804); and
combining (807) the manipulated dry signal component and the manipulated
reverberation signal component, or combining the dry signal component and the
manipulated reverberation signal component, or combining the manipulated dry
signal component and the reverberation signal component to obtain the mi signal
(808).
15. Computer program having a program code for performing, when running on a
computer, the method f claim 10 or claim 14.