Method for evaluating a useful signal and audio device

Abstract

A high-performance method evaluates a useful signal of an audio device, and in particular of an audio apparatus, for example for reducing interference. Accordingly, in the method at least two microphone signals are each obtained from a sound signal and a reference signal is obtained from the microphone signals, a portion of the microphone signals from a predetermined direction being blocked. The microphone signals are filtered by a filter such that an evaluation signal is obtained. To that end, a coherence value is determined from portions of the reference signal and a power density value is determined from the coherence value. The filter is parameterized on the basis of the power density value.

Claims

1. A method for estimating a useful signal from a hearing apparatus, which comprises the steps of: obtaining at least two microphone signals from a respective sound signal, wherein the microphone signals form a microphone signal vector; obtaining a reference signal vector from the microphone signal vector, the reference signal vector having a portion of the microphone signals from a prescribable direction in a blocked state; filtering the microphone signal vector using a filter, as a result of which an estimation signal is obtained as a useful signal; ascertaining a coherence variable from the reference signal vector and the microphone signal vector; ascertaining a power density variable from the coherence variable; and parameterizing the filter on a basis of the power density variable.

2. The method according to claim 1, wherein the step of obtaining the reference signal vector involves a prescribable direction of the useful signal being estimated from the microphone signal vector.

3. The method according to claim 2, which further comprises obtaining the reference signal vector by a directional blind source separation algorithm.

4. The method according to claim 1, wherein the step of obtaining the reference signal vector involves a respective useful signal component of each of the microphone signals being aligned with one another and then subtracted from one another.

5. The method according to claim 4, which further comprises aligning useful signal components with one another both in terms of delay and in terms of their spectra.

6. The method according to claim 1, wherein the coherence variable is a coherence matrix.

7. The method according to claim 1, wherein ascertaining the power density variable involves a use of the reference signal vector.

8. The method according to claim 1, wherein the useful signal is a voice signal.

9. The method according to claim 1, wherein the reference signal vector contains voice signal portions that are not part of the useful signal.

10. A hearing apparatus, comprising: a microphone device for obtaining at least two microphone signals from a respective sound signal, the microphone signals forming a microphone signal vector; a blocking device for obtaining a reference signal vector from the microphone signal vector, the reference signal vector having a portion of the microphone signals from a prescribable direction in a blocked state; a filter for filtering the microphone signal vector, as a result of which an estimation signal is obtained as a useful signal; and a computation device for ascertaining a coherence variable from the reference signal vector and the microphone signal vector and for ascertaining a power density variable from the coherence variable and for parameterizing said filter on a basis of the power density variable.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

(1) FIG. 1 is an illustration of a basic configuration of a hearing apparatus according to the prior art; and

(2) FIG. 2 is a block diagram of a system for estimating a useful signal according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

(3) The exemplary embodiments outlined in more detail below are preferred embodiments of the present invention.

(4) Referring now to the figures of the drawings in detail and first, particularly to FIG. 2 thereof, there is shown a method that can be implemented in a hearing aid as shown in FIG. 1 or in another hearing apparatus. Secondly, the blocks shown in FIG. 2 can represent corresponding devices of a hearing apparatus.

(5) An exemplary hearing apparatus or an exemplary hearing aid contains a sensor or microphone arrangement having at least two sensors or two microphones M1, Mp. The text below refers always to microphones by way of representation.

(6) Each microphone M1, Mp converts the respectively applied sound signal into a corresponding microphone signal. The sound signals are components of a sound field that represents the acoustic situation of a hearing aid wearer, for example. One such typical situation would be that of a “cafeteria scenario”, in which the hearing aid wearer speaks to a dialog partner, one or more other persons are speaking in the background and there is other background noise. Alternatively, there may be a different acoustic situation that involves non-steady noise.

(7) The microphone signals, which together form a microphone signal vector x, are each processed further in separate channels, i.e. one microphone signal is processed in each channel. FIG. 2 shows this multichannel processing by means of thick arrows. The microphone signal vector x is supplied to a source localization unit LOC (source localization) in the multichannel system 10. The source localization unit takes the microphone signal vector x and obtains position data φq for a source Sq. In particular, the position information φq from the useful signal source Sq is ascertained in three-dimensional space or simply just as an angle or an angle and distance. This position information φq is used as coarse reference information for creating a blocking matrix BM. The blocking matrix BM is used to spatially mask from the microphone signals or the microphone signal vector x those portions that come from the spatial area of the useful signal source. By way of example, such a blocking matrix BM can be based on a directional blind source separation algorithm, as described in Y. Zheng, K. Reindl and W. Kellermann “BSS for Improved Interference Estimation for Blind Speech Signal Extraction with Two Microphones,” in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Aruba, Dutch Antilles, December 2009. Alternatively, any other algorithms can be used for ascertaining the blocking matrix BM.

(8) Hence, a multichannel reference signal or a reference signal vector n is obtained from the microphone signal vector x by applying the blocking matrix BM. If the signals are subtracted in the blocking matrix in pairs, for example, the number of signals of the multidimensional reference signal vector n can correspond to half the number of microphone signals or channels. An uneven number of microphone signals preferably prompts rounding up. The reference signal vector is thus normally a multidimensional vector containing a plurality of individual signals.

(9) The reference signal vector n is supplied to a coherence estimation unit COH together with the microphone signal vector x that consists of the individual microphone signals. The coherence estimation unit estimates a coherence matrix Γ from the two vectors n and x. The coherence matrix Γ is supplied to a PSD estimation unit PSD. The PSD estimation unit estimates a multidimensional power density estimation variable S from the coherence matrix Γ and the reference vector n, as described, by way of example, in I. McCowan and H. Bourlard, “Microphone Array Post-Filter for Diffuse Noise Field,” in IEEE Int. conf. Acoustics, Speech, Signal Processing (ICASSP), 2002, pages 905-908 or in K. Reindl., Y. Zheng, A. Schwarz, S. Meier, R. Maas, A. Sehr, and W. Kellermann, “A Stereophonic Acoustic Signal Extraction Scheme for Noise and Reverberant Environments,” Computer Speech and Language, 2012.

(10) A multichannel filter FILT estimates filter parameters from the power density estimation variable S. The filter parameters are applied to the microphone signals or to the microphone signal vector x in the filter FILT, as a result of which the estimation signal q is obtained for the particular useful source or the useful signal.

(11) Hence, it is primarily possible to achieve estimation of a non-steady second-order statistical variable relating to noise components by means of PSD by using the coherence of the relevant noise components. In this case, it is particularly possible to equate the target voice components initially in all the channels (delay compensation and spectral alignment), so that the available channels contain almost identical target voice components. This alignment can be accomplished by using a directional blind source separation algorithm of the type cited above. From the resultant signals, it is possible, as has been illustrated in detail above, to estimate the noise signal coherence matrix, which for its part is used to estimate the noise PSD matrix S. According to the invention, estimation of the useful signal thus requires no restrictions for the temporal signal characteristics. In contrast to known and typically used concepts, which can be used only for noise signals that are sufficiently steady (over time), the present invention uses the circumstance that the respective acoustic scenario is steady in space in order to estimate the noise PSD matrix. In this case, it can be assumed that the space domain for any scenarios is sufficiently steady, in contrast to the time domain. The reason for this is that the changes in the coherence function are primarily dependent on the spatial properties, i.e. on the geometric arrangement of the sources and objects in the acoustic scene. By contrast, the changes in the coherence function have only little dependency on the temporal properties of the signals.

(12) In summary, this thus means that the method according to the invention or the hearing apparatus according to the invention is not limited to specific scenarios that relate to noise that is steady over time. Accordingly, the concept according to the invention makes it possible to use or implement powerful, multichannel noise reduction techniques for any scenarios in which noise suppression is necessary. A fundamental component of the invention is thus based on the insight of separating the estimation of the spatial coherence of noise signals from the estimation of the second-order temporal statistical variables (PSD of the noise components). In this case, the space/time coherence matrices can also be estimated continuously for scenarios with voice signals that are unsteady (over time).

(13) In one specific example, the filter used can be a multichannel Wiener filter. In principle, however, it is also possible to use a signal-channel filter. Such filtering can be used for noise suppression in a binaural hearing aid, for example.

(14) The PSD noise estimation together with the multichannel Wiener filter can be implemented in combination with a polyphase filter bank, as is typically used in hearing aids. The concept according to the invention can be realized on the basis of an SIR/SINR gain (signal to interference ratio/signal to interference and noise ratio). Furthermore, an ideal blind source separation scheme, for example, is assumed for the computation, i.e. the target voice components are approximately the same in all the available channels. Furthermore, in this specific case, it is possible to use ideal block-based voice activity detection (VAD) in order to estimate the noise coherence matrix.

(15) In experiments, it has been possible to show that if need be a plurality of interference or voice signals can be markedly reduced (SIR at least 10 dB). Even if additional (diffuse) background chatter was present, an SINR of 8 dB was able to be achieved. In this case, processing artifacts (noise in the individual signals) were inaudible.