Patent Description:
Binaural Room Impulse Responses (BRIRs) are necessary to create immersive 3D audio signals over headphones. The BRIRs depend not only on the persons, but also on the rooms. As is shown in <FIG>, a BRIR consists of the direct sound part, early reflections and late reverberation (on a time axis). The direct sound part is important for sound source localization. The early reflections provide spatial information, and are essential for perception of distance and externalization of sound sources. The late reverberation provides room information to listeners, which consists of a high density of reflections, and no longer depends on the position of sound sources.

Measuring the BRIRs for each user in common consumer scenarios is difficult and impractical, due to practical obstacles and complexities. For this reason, a set of synthesized BRIRs, e.g., based on a general head-related transfer function (HRTF) and artificial reverberation, physical room acoustic modeling, etc., or a set of reference BRIRs are usually used for binaural rendering, instead of measured BRIRs.

However, the perceived externalization and plausibility may be reduced when applying synthesized BRIRs without knowledge of the actual room acoustics for binaural rendering. This is due to the fact that the perceived auditory impression relies on the external sound stimuli, but also depends on the expectations about the auditory event in the actual room due to different room acoustics. It is thus important that the acoustic parameters in the actual real room are estimated as precisely as possible. One important acoustic parameter is the reverberation time (also RT60).

A number of conventional solutions regarding generally room adaptation of virtual 3D audio, and specifically estimation of reverberation time are known.

<CIT> uses the microphone and speaker of a smart device (e.g., Virtual Reality (VR) headset, smartphone, etc.) to measure a room impulse response (RIR) in an actual room, and then combine it with a pre-selected HRTF to render 3D audio. Thereby, the virtual acoustics can be adapted to the acoustics of the real actual room. However, the measurement is difficult to perform in common consumer scenarios, since the requirements of microphone and speaker in the device, and of the listening environment are relative high (noise floor, frequency responses, background noise of environment, signal-to-noise ratio (SNR) of recorded signal, etc.). Therefore, disadvantages of this approach are that:.

Instead of directly using a measured RIR as in the above approach, <CIT> proposes an approach for extending 3D audio rendering algorithms to match local environment acoustics by using static room parameters. For that, the reverberation fingerprint (volume and the frequency-dependent reverberation time) of the actual room is measured, and compared to the reverberation fingerprint of a reference room (already premeasured). After that, the BRIR of the actual room can be reshaped based on the reference BRIR and the reverberation fingerprint of the actual and reference room. This idea aims to adapt the virtual 3D audio to real acoustic environment. However, disadvantages of this approach are that:.

Conventional methods of estimating the reverberation time are typically based on measured RIR (Schroeder method) or recorded white noise (Interrupted Method). However, these measurements are difficult to perform in common consumer scenarios, since the requirements of playback, record devices, and listening environment are relative high, and the procedure of the measurement may be difficult for some consumers. To overcome these problems, some blind estimation methods of RT60 based on speech or music signal are proposed. The reverberation can be estimated using speech signal based on the maximum likelihood method/distribution of signal decay rates, etc. In particular, disadvantages of these conventional methods are that:.

The <CIT> describes how to provide a reverberation signal, using information about a reference impulse response from a reference environment and using characteristic information about reverberation decay in a local environment of the participant, wherein providing the reverberation signal can further include using information about a relationship between a volume of the reference environment and a volume of the local environment of the participant.

In view of the above-mentioned disadvantages, the invention aims to improve the conventional approaches and methods of generally room adaptation of virtual 3D audio, and specifically estimation of reverberation time. An objective of the invention is to provide a device and method for estimating room acoustic parameters faster and more efficiently. In particular, the device and method should be able to precisely estimate a full-band (i.e. not frequency-limited) reverberation time and optionally also a mixing time.

The objective of the invention is achieved by the solution provided in the enclosed independent claims. Advantageous implementations of the invention are further defined in the dependent claims.

In particular, embodiments of the invention obtain the room acoustic parameters by measuring speech signals in an actual room. Then the BRIR can be synthesized based on the estimated acoustic parameters. The synthesized BRIR can further be used in binaural rendering, for instance, for AR applications or headphone surround on mobile devices. Embodiments of the invention are based particularly on a solution for extending a blind estimation of a frequency-dependent reverberation time from lower frequencies to higher frequencies.

A first aspect of the invention provides a device for estimating acoustic parameters, the device being configured to record an acoustic signal, particularly a speech signal, estimate a frequency-dependent reverberation time in a lower frequency range based on the recorded acoustic signal, and extend the frequency-dependent reverberation time to a higher frequency range based on a predetermined model to obtain an extended frequency-dependent reverberation time, wherein the lower frequency range includes frequencies below <NUM>, and the higher frequency range includes frequencies above <NUM>.

The device of the first aspect does not measure the (room) acoustic parameters (i.e. particularly the reverberation time), but estimates them based on speech recording. Thus, no overly quiet environment and good equipment (very high SNR) are required. The device consequently works well also in noisy environment. Further, prior knowledge of the room geometry and the reverberation time are not necessary. Thus, no additional device or software is needed to measure the room volume. The device of the first aspect differs from conventional methods of estimating the reverberation time, since the extended frequency-dependent reverberation time covers the full frequency range, while the conventional methods are frequency-limited.

In an implementation form of the first aspect, the lower frequency range includes frequencies between <NUM> and <NUM>, and the higher frequency range includes frequencies between <NUM> and <NUM>.

That is, the device is able to obtain a reverberation time even above <NUM>, unlike the conventional methods for estimating the reverberation time.

In a further implementation form of the first aspect, the device is configured to estimate the frequency-dependent reverberation time in the lower frequency range by performing blind estimation.

Due to the blind estimation based on e.g. speech signal or music signals, measuring a RIR (Schroeder method) or recorded white noise (Interrupted Method) is not required. The reverberation time can instead be estimated using the acoustic signals based on, for example, a maximum likelihood method/distribution of signal decay rates, etc. The major applications are De-reverberation, enhancement of speech intelligibility, etc..

In a further implementation form of the first aspect, the predetermined model describes a reverberation time change from lower frequencies included in the lower frequency range to higher frequencies included in the higher frequency range.

The model can be pre-defined, such that the device can quickly and precisely estimate the extended reverberation time.

In a further implementation form of the first aspect, the device is configured to build the predetermined model by analyzing RIRs of multiple different room types.

Thus, the model includes a precise fingerprint of multiple different room types and geometries, and the device can estimate reverberation time precisely in any room.

In a further implementation form of the first aspect, the device is configured to smooth the frequency-dependent reverberation time over the lower frequency range before extending it to the higher frequency range.

This helps to reduce inaccuracies of the blind estimation caused by environment noises.

In a further implementation form of the first aspect, the device is configured to calculate coefficients for the predetermined model from a single reverberation time of the smoothed frequency-dependent reverberation time at a determined frequency in the lower frequency range, in order to extend it to the higher frequency range based on the predetermined model.

Based on these parameters, the extended reverberation time can be precisely estimated.

In a further implementation form of the first aspect, the device is configured to estimate the frequency-dependent reverberation time in the lower frequency range by: filtering the recorded acoustic signal with a filter bank, and estimating a reverberation time in each of multiple frequency channels of the filtered acoustic signal based on a blind estimation method.

For instance, a maximum likelihood method or an estimation of power spectral density may be used for the blind estimation method.

In a further implementation form of the first aspect, the device is configured to estimate a mixing time based on the extended frequency-dependent reverberation time.

Accordingly, the device of the first aspect obtains also the mixing time as part of the estimated room acoustic parameters. That is, the mixing time is not fixed and is room related, thus leading to improved results when synthesizing BRIRs.

In a further implementation form of the first aspect, the device is configured to estimate the mixing time by: multiplying a single reverberation time of the extended frequency-dependent reverberation time at a determined frequency in the lower frequency range by a predetermined factor, or calculating a room volume based on the extended frequency-dependent reverberation time and calculating the mixing time based on the room volume.

That is, two ways of obtaining the mixing time are provided, the formed focusing on speed, the latter on precision.

In a further implementation form of the first aspect, the device is configured to synthesize a BRIR based on the extended frequency-dependent reverberation time.

The synthesized BRIR is thus obtained with knowledge of the actual room acoustics and leads to an improved binaural rendering.

In a further implementation form of the first aspect, the device is configured to synthesize the BRIR based further on the mixing time.

This further improved the BRIR in terms of correspondence to the actual room.

In a further implementation form of the first aspect, the device is configured to synthesize the BRIR by: using the extended frequency-dependent reverberation time to synthesize a late reverberation part of the BRIR, and using the mixing time to adjust the start time of the late reverberation part in the synthesized BRIR.

The mixing time is defined by the transition point (in time) from early reflections to late reverberation. Thus a very precise BRIR adapted to the actual room is obtained.

In a further implementation form of the first aspect, which is not part of the claims, the device is configured to synthesize the BRIR by: reshaping white noise or white Gaussian noise according to the extended frequency-dependent reverberation time to synthesize the late reverberation part, truncate the late reverberation part according to the mixing time and a window function, and combine a HRTF for a direct part and/or early reflection with the truncated later reverberation part to obtain the entire BRIR.

A second aspect of the invention provides a method for estimating acoustic parameters, the method comprising: recording an acoustic signal, particularly a speech signal, estimating a frequency-dependent reverberation time in a lower frequency range based on the recorded acoustic signal, and extending the frequency-dependent reverberation time to a higher frequency range based on a predetermined model to obtain an extended frequency-dependent reverberation time, wherein the lower frequency range includes frequencies below <NUM>, and the higher frequency range includes frequencies above <NUM>.

In an implementation form of the second aspect, the lower frequency range includes frequencies between <NUM> and <NUM>, and the higher frequency range includes frequencies between <NUM> and <NUM>.

In a further implementation form of the second aspect, the method comprises estimating the frequency-dependent reverberation time in the lower frequency range by performing blind estimation.

In a further implementation form of the second aspect, the predetermined model describes a reverberation time change from lower frequencies included in the lower frequency range to higher frequencies included in the higher frequency range.

In a further implementation form of the second aspect, the method comprises building the predetermined model by analyzing RIRs of multiple different room types.

In a further implementation form of the second aspect, the method comprises smoothing the frequency-dependent reverberation time over the lower frequency range before extending it to the higher frequency range.

In a further implementation form of the second aspect, the method comprises calculating coefficients for the predetermined model from a single reverberation time of the smoothed frequency-dependent reverberation time at a determined frequency in the lower frequency range, in order to extend it to the higher frequency range based on the predetermined model.

In a further implementation form of the second aspect, the method comprises estimating the frequency-dependent reverberation time in the lower frequency range by: filtering the recorded acoustic signal with a filter bank, and estimating a reverberation time in each of multiple frequency channels of the filtered acoustic signal based on a blind estimation method.

In a further implementation form of the second aspect, the method comprises estimating a mixing time based on the extended frequency-dependent reverberation time.

In a further implementation form of the second aspect, the method comprises estimating the mixing time by: multiplying a single reverberation time of the extended frequency-dependent reverberation time at a determined frequency in the lower frequency range by a predetermined factor, or calculating a room volume based on the extended frequency-dependent reverberation time and calculating the mixing time based on the room volume.

In a further implementation form of the second aspect, the method comprises synthesizing a BRIR based on the extended frequency-dependent reverberation time.

In a further implementation form of the second aspect, the method comprises synthesizing the BRIR based further on the mixing time.

In a further implementation form of the second aspect, the method comprises synthesizing the BRIR by: using the extended frequency-dependent reverberation time to synthesize a late reverberation part of the BRIR, and using the mixing time to adjust the start time of the late reverberation part in the synthesized BRIR.

In a further implementation form of the second aspect, which is not part of the claims, the method comprises synthesizing the BRIR by: reshaping white noise or white Gaussian noise according to the extended frequency-dependent reverberation time to synthesize the late reverberation part, truncate the late reverberation part according to the mixing time and a window function, and combine a HRTF for a direct part and/or early reflection with the truncated later reverberation part to obtain the entire BRIR.

With the method of the second aspect and its implementation forms, the advantages and effects described above for the device of the first aspect and its respective implementation forms are achieved.

A third aspect of the invention provides a computer program product comprising program code for controlling a device to perform the method according to the second aspect when the program code is executed by one or more processors of the device.

The above described aspects and implementation forms of the invention will be explained in the following description of specific embodiments in relation to the enclosed drawings, in which.

<FIG> shows a device <NUM> according to an embodiment of the invention. The device <NUM> is configured to estimate room acoustic parameters, in particular a reverberation time of an actual room.

The device <NUM> may comprise processing circuitry <NUM> configured to perform the various operations and methods described below. The processing circuitry <NUM> may comprise hardware and software. In one embodiment, the processing circuitry <NUM> comprises one or more processors (not shown) and a non-volatile memory (not shown) connected to the one or more processors. The non-volatile memory may carry executable program code which, when executed by the one or more processors, causes the device <NUM> to perform said operations or methods.

The device <NUM> is configured to record <NUM> an acoustic signal <NUM>, particularly a speech signal. The recording <NUM> may be done by means of a mono microphone, binaural microphone or the like. By recording the acoustic signal <NUM> it becomes a recorded acoustic signal <NUM>.

The device <NUM> is further configured to estimate <NUM> a frequency-dependent reverberation time <NUM> in a lower frequency range based on the recorded acoustic signal <NUM>. The estimating <NUM> may be performed by the processing circuitry <NUM>, which may include a filter bank (not shown). The lower frequency range may include frequencies below <NUM>, for example frequencies between <NUM> and <NUM>. The estimation <NUM> may be done by blind estimation.

The device <NUM> is further configured to extend <NUM> the estimated frequency-dependent reverberation time <NUM> for the lower frequency range to a higher frequency range, wherein the extending <NUM> bases on a predetermined model <NUM>. Thereby, an extended frequency-dependent reverberation time <NUM> is obtained. The extending <NUM> may be performed by the processing circuitry <NUM> which may include one or more smoothing filters (not shown). The higher frequency range may include frequencies above <NUM>, for example, frequencies between <NUM> and <NUM>. The predetermined model <NUM> may describe a reverberation time change from the lower frequencies included in the lower frequency range to the higher frequencies included in the higher frequency range. The model <NUM> may be built, by the device <NUM> or beforehand by another device, by analyzing RIRs of multiple different rooms (types, geometries, sizes).

<FIG> shows a general method <NUM> according to an embodiment of the invention for estimating acoustic parameters of a room, particularly the reverberation time. The method <NUM> may be performed by the device <NUM> of <FIG>. The method <NUM> comprises a first step of recording <NUM> an acoustic signal <NUM>, particularly a speech signal. Further, it comprises a second step of estimating <NUM> a frequency-dependent reverberation time <NUM> in a lower frequency range based on the recorded acoustic signal <NUM>. Further, it comprises a third step of extending <NUM> the frequency-dependent reverberation time <NUM> to a higher frequency range based on a predetermined model <NUM>, in order to obtain an extended frequency-dependent reverberation time <NUM>.

More details of the device <NUM> (and correspondingly the method <NUM>) are described below according to further embodiments of the invention, wherein all embodiments build on the general embodiment of the device <NUM> shown in <FIG>. Accordingly, same elements and functions in the various embodiments share the same reference signs.

<FIG> shows an overview of an analysis part and a synthesis part, respectively, in a device <NUM> according to an embodiment of the invention. The analysis part includes a signal playback <NUM>, e.g. of dry speech signal, includes recording <NUM> of the signal, and includes estimation <NUM> of room acoustic parameters. The estimation <NUM> includes the estimation <NUM> and extension <NUM> of the reverberation time shown in <FIG>, i.e. obtains the extended reverberation time <NUM>. The synthesis part includes a synthesis <NUM> of BRIRs according to the estimated room acoustic parameters, particularly based on the extended reverberation time <NUM> and optionally on a mixing time <NUM> (described in detail with respect to <FIG>). The analysis part can be simply done by a user using microphone(s) on a headset or smart device, and the synthesis part can be performed on the smart device. The details of the analysis part and synthesis part, respectively, are discussed in the following.

<FIG> shows an exemplary overview of the structure of the analysis part of a device <NUM> according to embodiment of the invention. A piece of speech signal <NUM> is recorded <NUM>, for instance, with a mono microphone or a pair of binaural microphones. Then the frequency-dependent reverberation time <NUM> may be blindly estimated <NUM> based on the recorded signal(s) <NUM>. After that, the estimated reverberation time <NUM> may be smoothed <NUM>, in <FIG> exemplarily from <NUM> to <NUM>, and is then extended <NUM> from e.g. <NUM> to <NUM>, in order to obtain the extended frequency-dependent reverberation time <NUM>. In addition, the mixing time <NUM> may be estimated <NUM> based on the obtained extended reverberation time <NUM>.

An example of the blind estimation <NUM> shown in <FIG> is further illustrated in <FIG>. To blindly estimate <NUM> the frequency-dependent reverberation time <NUM>, a piece of speech signals <NUM> is again recorded <NUM>, for instance, with a mono or a pair of binaural microphones. Then the recorded signals <NUM> are filtered through a filter bank <NUM>. For example, a gammatone filter bank or a <NUM>/<NUM> octave filter bank may be used. Then, the frequency-dependent reverberation time <NUM> is estimated using, for example, a maximum likelihood method for the signals in each frequency channel. This may specifically be done as it is described in '<NPL>' or as it is described in '<NPL>.

However, the estimated reverberation time <NUM> is still frequency-limited up to <NUM>, due to the frequency range of the speech signals <NUM>. In addition, in the case of a noisy environment, the accuracy of the obtained reverberation time may not be overly robust in low to mid frequencies (e.g., from <NUM> to <NUM>). Due to the lack of a blindly estimated reverberation time in mid to high frequencies, it would be difficult to exactly synthesize the late reverberation part in a BRIR based on the reverberation time <NUM>. Therefore, the device <NUM> is further configured to obtain a frequency-dependent reverberation time <NUM> in a full frequency range (e.g. <NUM>-<NUM>). To this end, the device <NUM> is configured to extend <NUM> (here smooth and extend <NUM>) the reverberation time <NUM>, in order to obtain the extended frequency-dependent reverberation time <NUM>.

<FIG> shows an example of extending <NUM> the reverberation time <NUM> from mid to high frequencies in a device <NUM> according to an embodiment of the invention. Through analyzing large databases of RIRs <NUM>, e.g., an AIR RIR dataset, it can be found that the reverberation time changes smoothly from mid to high frequencies. However, in the case of a noisy environment, the accuracy of blindly estimating <NUM> a reverberation time <NUM>, for example, from <NUM> to <NUM>, may be somewhat reduced. Therefore, a smoothing filter <NUM> may be applied to the estimated reverberation time <NUM>, for example, from <NUM> to <NUM>, in order to reduce the inaccuracies of measurements caused by environment noises. For example, a median filter can be applied to smooth <NUM> the reverberation time <NUM>, but it is also possible to use another smoothing method or filter. Moreover, through analyzing large databases of RIRs <NUM>, it can also be found that the reverberation time decreases monotonically from mid to high frequencies. Therefore, a model <NUM> of reverberation time from mid to high frequencies can be built <NUM> according to the frequency-dependent reverberation time obtained in different rooms (wherein the reverberation time may be calculated using the Schroeder method for RIRs <NUM> in different rooms from the RIR dataset). The model <NUM> may particularly be expressed as: <MAT>.

T<NUM>,mid to high is the reverberation time in mid to high frequencies, T<NUM>,<NUM> kHz is the reverberation time at <NUM>, and fmid to high denotes mid to high center frequencies of the filter bank <NUM>. The parameter a is usually larger than <NUM>, while b is usually smaller than <NUM>. These parameters can be obtained using the following equations: <MAT> <MAT>.

These equations (Eq. <NUM> and <NUM>) for calculating the parameters a and b may be determined through analyzing large database of RIRs <NUM>. The parameters c<NUM>, c<NUM>, d<NUM>, d<NUM>, e<NUM>, e<NUM>, g<NUM> and g<NUM> are, for example: <NUM>, -<NUM>, -<NUM>, <NUM>, <NUM>, -<NUM>, - <NUM>, <NUM>, which values may be determined based on an AIR RIR database. For an actual real room, the reverberation time at <NUM> (T<NUM>,<NUM> kHz) should first be estimated, then the reverberation time at mid to high frequencies may be calculated based on the model <NUM> (Eq. <NUM>). It is also possible to use other models <NUM> (e.g. fitting functions) to calculate the extended reverberation time <NUM> in mid to high frequencies, e.g., exponential function, higher order polynomial function etc..

In summary, smoothing of the reverberation time <NUM> from, for example, <NUM> to <NUM>, may be performed after the blind estimation <NUM> of the reverberation time <NUM> using, for instance, the maximum likelihood method. Then, the reverberation time at <NUM> may be used to calculate the coefficients a and b. After that, the extended reverberation time <NUM>, for example, from <NUM> to <NUM> may be obtained based on the proposed model <NUM> (Eq. <NUM>).

The mixing time <NUM> describes the time of transition from early reflections to late reverberation in RIRsBRIRs (see <FIG> in the exemplary BRIR). A device <NUM> according to an embodiment of the invention may be further configured to estimate <NUM> the mixing time <NUM>, in order to adapt the late reverberation part in the synthesized BRIR obtained by using blindly estimated reverberation time <NUM>.

<FIG> shows two exemplary ways of calculating the mixing time <NUM> according to the estimated extended reverberation time <NUM>. In one way, the mixing time is directly predicted <NUM> based on the reverberation time <NUM> measured at <NUM>, which can be approximated as <NUM> × reverberation time. This may be done as it is described in <NPL>'. Another way is to first to predict <NUM> the room volume <NUM> according to the estimated extended reverberation time <NUM>, which may be done as it is described in '<NPL>', and then to calculate <NUM> the mixing time <NUM> based on the predicted room volume <NUM>, which may be done as it is described in <NPL>'.

Based on the estimated room acoustic parameters of the actual room (e.g., frequency-dependent extended reverberation time <NUM> and optionally the mixing time <NUM>), it is possible to synthesize BRIRs, which adapt very well to the actual room.

An example of how to synthesize the BRIRs in a device <NUM> according to an embodiment of the invention is shown in <FIG>. The frequency-dependent extended reverberation time <NUM> is used to synthesize the late reverberation part <NUM> in BRIRs, and the mixing time <NUM> may be used to adapt the late reverberation to the BRIRs.

In particular, as shown in <FIG>, a pair of dual-channel white Gaussian noise <NUM> (for the left and right ear) may first be filtered through a filter bank <NUM>. It may be advantageous to use a filter bank <NUM> similar or identical to the filter bank <NUM>, which is applied for the analysis part, i.e. the reverberation time estimation. Then, the filtered white Gaussian noise <NUM> may be reshaped <NUM> according to the frequency-dependent reverberation time <NUM> in each frequency channel. For example, the filtered white Gaussian noise <NUM> can be reshaped <NUM> by multiplying with an exponential function h(f), and the decay rate of this exponential function ρ(f) depends on the reverberation time: <MAT>.

A is the scaling factor of the late reverberation, which depends on the source-listener distance, and is usually limited between <NUM> and <NUM>. Further, n is the sample number, and fs is the sampling frequency. For example, A, n, and fs can be set to <NUM>, <NUM> samples, and <NUM>, respectively. Then, the reshaped Gaussian white noise <NUM> in each frequency channel may be summed up <NUM> to obtain the synthesized reverberation <NUM> for the left and right ear.

After that, the obtained reverberation <NUM> can be further truncated <NUM> by a window based on the estimated mixing time <NUM> to adapt to the synthesized BRIRs. To guarantee smooth transitions between early reflections and late reverberation without perceptible artefacts, a window for example with <NUM> long rise time may be applied to truncate <NUM> the synthesized late reverberation. Thereby, a synthesized late reverberation <NUM> after windowing is obtained, based on which the BRIRs can be synthesized <NUM>.

The performance of the device <NUM> (and correspondingly the method <NUM>) to obtain the full frequency band reverberation time <NUM> is illustrated by simulation results in <FIG> and <FIG>. For these simulations, a piece of speech signal (sampling frequency <NUM>) was convolved with a RIR of a lecture room taken from the AIR RIR dataset, as it is described in `<NPL>' (downsampled to <NUM>). To simulate the environment noise, white Gaussian noise with different SNRs (<NUM> dB, <NUM> dB, <NUM> dB, <NUM> dB and <NUM> dB) was added into the reverberant speech signal. <NUM> dB SNR represents a quite noisy environment, while <NUM> dB SNR represents a relative quiet environment. The reverberation time calculated by the Schroeder method was used as the baseline (reference). This Schroeder method is based on the analysis of the known RIR <NUM>, and is conventionally used to calculate the reverberation time. In contrast, the device <NUM> and method <NUM> according to embodiments of the invention are based on a piece of recorded speech signals <NUM> without measuring the RIR in the actual room.

<FIG> shows particularly the results of a blindly estimated reverberation time using a conventional method. It can be seen that the reverberation time is limited up to <NUM>, due to the frequency range of the speech signal, and the estimated reverberation time in low to mid frequencies (i.e., e.g., from <NUM> to <NUM>) is not stable in a noisy environment (low SNRs).

<FIG> shows the results of the estimated reverberation time using the device <NUM> (or correspondingly method <NUM>) according to embodiments of the invention. It is easy to see that the accuracy of the estimated reverberation time for low SNRs is improved by smoothing the reverberation time, here for <NUM> to <NUM>. In addition, the reverberation time is extended, here from <NUM> to <NUM>, and is matched well to the baseline (Schroeder Method).

A BRIR can be considered as the sum of direct sound, early reflections and late reverberation. <FIG> shows an example of a synthesized BRIR of left ear using generic/non-individual HRTF for direct sound, simulated early reflection and synthesized late reverberation obtained by the device <NUM> (or correspondingly method <NUM>) according to embodiments of the invention. More details are given below.

In the following, devices <NUM> according to specific embodiments of the invention are described. The embodiments are divided into two parts: Firstly, analysis of reverberation time (specific embodiments <NUM> and <NUM>) and, secondly, synthesis of BRIRs (specific embodiments <NUM>, <NUM> and <NUM>).

Embodiment <NUM> is shown in <FIG> and <FIG>, respectively, and bases on an analysis of the reverberation time using a mono microphone. As shown in <FIG>, the user speaks, for example, some short sentences (<NUM>~<NUM>) and at the same time records sound using the device <NUM>, e.g. a smart device with a microphone (e.g., a smartphone or a tablet) in the actual room. Since the device <NUM> also works well in quite noisy environment (e.g., SNR 15dB of the recorded signal), the sound source does not have to be positioned very close to the microphone, and also the environment does not have to be very quiet. The device <NUM> estimates room acoustic parameters (reverberation time <NUM>, mixing time <NUM>, etc.) based on the recorded sound. The device <NUM> then synthesizes the late reverberation <NUM> for the left and the right ear based on the estimated room acoustic parameters (e.g., reverberation time <NUM> for left and right ears). <FIG> shows a block diagram for the device <NUM> of this embodiment. The details of the block are as described above with respect to <FIG>, <FIG> and <FIG>.

Embodiment <NUM> is shown in <FIG> and <FIG>, respectively, and bases on an analysis of the reverberation time using a pair of binaural microphones. As shown in <FIG>, the user may play back a dry speech file on the device <NUM>, e.g. a smart device (e.g., a smartphone or tablet), or may speak some short sentences (<NUM> ∼ <NUM>) in the actual room and at the same time records sound using a pair of binaural microphones, for example, microphones on an Active Noise Control (ANC) headphone. The recorded binaural signals are used by the device <NUM> to estimate the room acoustic parameters (e.g., reverberation time <NUM>, mixing time <NUM>, etc.) separately for the left and the right ear. The calculated room acoustic parameters for the left and the right ear are further used to synthesize the reverberation <NUM> for the left and right ears separately. <FIG> shows a block diagram for the device <NUM> of this embodiment. The details of the block are as described above with respect to <FIG>, <FIG> and <FIG>.

Embodiment <NUM> is shown in <FIG> and <FIG>, respectively, and bases on synthesis of BRIRs using non-individual HRTF and late reverberation. A BRIR can be considered as the sum of direct sound, early reflections and late reverberation. As shown in <FIG>, the simplest way to synthesize a pair of BRIR is by using and combining <NUM> the general/non-individual HRTF <NUM> for direct sound and the synthesized late reverberation <NUM>. In that way the early reflections are neglected, and there is no need to know the exact mixing time <NUM>. Though the perceived externalization and plausibility may be reduced due to the lack of early reflection, the property of room (size of the room, reverberation of the room, etc.) can still be perceived. <FIG> shows the synthesized BRIRs consisting of the direct sound and late reverberation.

Embodiment <NUM> is shown in <FIG> and <FIG>, respectively, and bases on synthesis of BRIRs using reference BRIR and synthesized late reverberation. Late reverberation provides the room information, which is useful for listeners to perceive the acoustic environment. Early reflections provide spatial information, which are important for externalization of virtual sound sources. In this embodiment, as shown in <FIG>, a set of reference BRIRs <NUM> is used to synthesize <NUM> the BRIRs in the actual room. In that way, the direct sound and early reflections <NUM> are directly taken from the reference BRIRs <NUM>, and the late reverberation part in the reference BRIRs are removed <NUM> and replaced with the synthesized late reverberation <NUM> after windowing (truncating <NUM>) based on the later reverberation <NUM> derived from the estimated room acoustic parameters (frequency-dependent reverberation time <NUM> and mixing time <NUM>). <FIG> shows an example of synthesized BRIR based on the reference BRIR <NUM> and the synthesized late reverberation <NUM>.

Embodiment <NUM> is shown in <FIG> and <FIG>, respectively, and bases on synthesis of BRIRs using non-individual HRTF, early reflections and late reverberation. In this embodiment, BRIR is considered as the sum of direct sound, early reflections and late reverberation. As shown in <FIG>, general/non-individual HRTF <NUM> is used to generate the direct sound part. Given additional information <NUM> of room geometry, position of sound source(s) and listener, general/non-individual HRTF <NUM> is also used to simulate <NUM> the early reflections <NUM>. The synthesized late reverberation <NUM> with actual room acoustic is adapted to the BRIRs.

Claim 1:
A device (<NUM>) for estimating acoustic parameters, wherein the device (<NUM>) is configured to
record (<NUM>) an acoustic signal (<NUM>), particularly a speech signal,
estimate (<NUM>) a frequency-dependent reverberation time (<NUM>) in a lower frequency range based on the recorded acoustic signal (<NUM>), and
extend (<NUM>) the frequency-dependent reverberation time (<NUM>) to a higher frequency range based on a predetermined model (<NUM>) to obtain an extended frequency-dependent reverberation time (<NUM>),
wherein the lower frequency range includes frequencies below <NUM>, and the higher frequency range includes frequencies above <NUM>, and
wherein the device (<NUM>) is further configured to:
smooth (<NUM>) the frequency-dependent reverberation time (<NUM>) over the lower frequency range before extending (<NUM>) it to the higher frequency range.