Method for reducing noise using trainable models

The object is to improve the effect of a noise reduction algorithm for hearing apparatuses and in particular hearing aids. This is achieved by a method wherein the input signal is modeled by a wanted signal model and a noise signal model. In addition, a signal statistic of the input signal is recorded in a data logging unit. The wanted signal model and/or the noise signal model can now be changed as a function of said signal statistic. Finally the noise component of the input signal is reduced using the noise signal model and/or the wanted signal model. This means that the models used can be continuously adapted to the hearing apparatus user's current situation.

FIELD OF THE INVENTION

The present invention relates to a method for reducing noise in hearing apparatuses by picking up an input signal, modeling the signal using a wanted signal model and a noise signal model, and reducing the noise component of the input signal using the unwanted sound estimated by the noise signal model. The term “hearing apparatus” is understood here as meaning in particular any device that can be worn in or on the ear, such as a hearing aid, a headset, headphones or the like.

BACKGROUND OF THE INVENTION

Hearing aids are portable hearing apparatuses for use by the hard of hearing. In order to meet the numerous individual requirements, different hearing aids types are available, such as behind-the-ear (BTE) hearing aids, in-the-ear (ITE) hearing aids, and concha hearing aids. The hearing devices listed by way of example are worn in the outer ear or in the auditory canal. However, bone conduction hearing aids, implantable or vibrotactile hearing aids are also commercially available. In these cases, the damaged hearing is stimulated either mechanically or electrically.

The basic components of a hearing aid are essentially an input transducer, an amplifier and an output transducer. The input transducer is generally a sound receiver, e.g. a microphone, and/or an electromagnetic receiver such as an induction coil. The output transducer is mainly implemented as an electroacoustic transducer, e.g. a miniature loudspeaker, or as an electromechanical transducer such as a bone conduction earpiece. The amplifier is usually incorporated in a signal processing unit. This basic design is shown in FIG.1using the example of a behind-the-ear hearing aid. Installed in a hearing aid housing1for wearing behind-the-ear are one or more microphones2for picking up sound from the environment. A signal processing unit3which is likewise incorporated in the hearing aid housing1processes the microphone signals and amplifies them. The output signal of the signal processing unit3is transmitted to a loudspeaker or earpiece4which outputs an audible signal. The sound is in some cases transmitted to the wearer's eardrum via a sound tube which is fixed in the auditory canal using an earmold. The hearing aid and in particular the signal processing unit3are powered by a battery5likewise incorporated in the hearing aid housing1.

Monaural noise reduction methods are a fixed component of hearing aids. Frequency-domain methods using spectral weighting, e.g. Wiener filter or spectral subtraction, are used for this purpose.

With these noise reduction methods, the noise component must be estimated from the received noisy signal. For this estimation, the minimum statistics method, for example, can be used. In addition to noise estimation, estimation of the amplitude spectrum of the wanted signal is also necessary for Ephraim-Malah spectral weighting.

Both the wanted signal estimation algorithms and the noise signal estimation algorithms are based on particular, mainly simplifying assumptions in respect of the signal statistic. Thus, for example, for determining the Ephraim-Malah weighting rules, the wanted signal amplitude spectra are assumed to be Gauss distributed (cf. EPHRAIM, Y.; MALAH, D.: Speech Enhancement Using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator. In: IEEE Transactions on Acoustics, Speech and Signal Processing, December 1984, Vol. ASSP-32 No. 6, pages 1109-1121).

However, the actual statistical characteristics of the wanted and noise signal are usually much more complex and are therefore only taken into consideration to a limited extent in the methods mentioned. In addition, the parameters for optimum noise reduction effect with minimal wanted signal distortion are normally in fixed settings during operation.

In the case of non-static noise, the effect of the noise reduction methods mentioned is severely limited. Because the signal statistic has to be acquired over a sufficiently long time, the estimation of a high time-domain dynamic range of the noise can follow only relatively slowly. This reduces the noise reduction effect in such situations.

With the hitherto known methods, no a priori available information is utilized for acquiring the signal statistic. Even if the signal statistic is taken into account, in all the methods only a finite number of statistically different signal models are used during operation. These signal models are all in a fixed form. In addition, the modeling, particularly for the wanted signal, is very complex and also mainly defined for one type of signal such as voice. The noise modeling is also mainly limited to the spectral envelope only. This means that in the case generally arising in practice, a plurality of spatially separated noise signals can be mapped only with great difficulty. Both the spatial and the spectral characteristics may also change over time.

Publication DE 101 14 101 A1 discloses a method for processing an input signal in a signal processing unit of a hearing aid. In the hearing aid, adjustment parameters of a signal processing unit which relate to noise reduction are set as a function of the result of signal analysis of the input signal. If noise signals are detected, they are assigned to different noise signal categories. Different noise reduction algorithms are activated and deactivated depending on the noise signal category determined.

SUMMARY OF THE INVENTION

The object of the present invention is therefore to improve the effect of noise reduction methods.

To achieve this object there is proposed according to the invention a method for reducing noise in hearing apparatuses by picking up an input signal, modeling the input signal with a wanted signal model and a noise signal model and reducing the noise component of the input signal using the noise signal model and/or the wanted signal model, and also acquiring a signal statistic of the input signal and changing the wanted signal model and/or the noise signal model as a function of the signal statistic. In this context the term “changing” is to be understood not as “replacing” a model, but as modifying and adapting the content of a model.

The invention is advantageously based on the recognition that a priori available information for acquiring the signal statistic can be used for obtaining the parameters of suitable models of the wanted signal and of the noise signal, the fixed model parameters having to be set using statistically relevant training data in such a way that maximally comprehensive mapping of the signal statistic is achieved. The inventive noise reduction is not therefore performed as in known methods using fixed assumptions with regard to signal estimation or using signal model parameters that have been pre-trained in a fixed manner. On the contrary, by acquiring the individual wanted and noise signal statistic, the noise reduction can be optimally matched to the current situation of the hearing aid wearer or hearing apparatus user.

According to a specific embodiment, one or both of the wanted signal model and noise signal model of the inventive noise reduction algorithm can be autoregressive models with trained codebooks, models with overcomplete codebooks, models based on transformations or wavelet representations, models with decompositions into tonal, transient and noise-like components and signal statistical modelings. This means that the models to be trained can be initiated with “pre-knowledge”.

According to another embodiment it can be provided that, during operation of the hearing apparatus, data logging of the input signal and/or of its signal statistic relating to parameters of the model to be changed is carried out and the model to be changed is trained using the logged data. Using the logged data, training can thus take place in real time. Preferably, data logging and training take place automatically in a continuous manner. A current newly trained signal model is therefore always available.

A noise reduction quality metric can be used for selecting a wanted signal model and a noise signal model.

In addition to the wanted signal model and the noise signal model, at least one other model selectable by the hearing apparatus user can be trained and used for noise reduction instead of the wanted signal model or noise signal model. The user can therefore himself be involved in the process of deciding on the model to be used and subjectively influence noise reduction.

According to another embodiment of the inventive method, the model to be changed can also be changed on the basis of a noise or wanted signal estimation carried out in real time, thereby also enabling model parameters to be obtained by estimations.

Another preferred embodiment of the present invention consists in that at least one other model is used to estimate the unwanted or wanted sound in addition to the noise signal model and the wanted signal model. Thus, for example, by using a plurality of parallel noise signal models, even complex noise originating from a plurality of different sources can be effectively suppressed.

DETAILED DESCRIPTION OF THE INVENTION

The exemplary embodiments described in greater detail below represent preferred embodiments of the present invention.

The noise suppression systems presented here generally relate to systems in which at least one noisy input signal is simulated by modeling, at least one model being used for a wanted signal component and a noise signal component in each case, the parameters of which are estimated as a function of the input signal such that the model optimally describes the input signal according to a particular criterion. Possible models typically include autoregressive models with trained codebooks as well as models with overcomplete codebooks, models based on transformations such as the Fourier transformation, the discrete cosine transformation or based on wavelet representations, models with decompositions into tonal, transient and noise-like components, signal statistical modeling or other suitable models. Using the thus obtained model-like descriptions for the wanted and noise signal, noise suppression can be carried out by means of various known techniques.

For the inventive noise suppression, one or more signal models are suitably adapted individually to the input signal statistic actually present. For this purpose there exist fundamentally different adaptation possibilities, as described in greater detail below in connection withFIGS. 2 to 4.

The system as shown inFIG. 2has a model-based noise reduction algorithm10as a central component. An input signal E is fed to it and it produces a corresponding output signal A. The noise reduction algorithm10is based on the one hand on a wanted signal model11and on the other on a noise signal model12. It additionally supplies a data logging unit13in which the input signal E is also logged. Logged model parameters M, logged quality metrics Q as well as the logged input signal E can therefore be read out from data logging unit13.

During operation of the hearing apparatus or more specifically hearing aid, the input signal and/or its signal statistic which is mapped by the corresponding model parameters are recorded by means of data logging in the data logging unit13. The logging can take place continuously or else as a function of the quality of the noise reduction currently achieved. A corresponding quality metric Q is constantly available and can initiate logging e.g. if a threshold is undershot. However, logging can also, for example, be initiated manually by the user.

Using the logged data, the training for improved model parameters M of the wanted signal and/or noise signal can then take place at the time of evaluation at the hearing aid acoustician's. This subsequent training is indicated by the arrow14inFIG. 2. Depending on the frequency of the logging periods, in the event of a simultaneously bad quality metric, the signal models already being used can be exchanged for the newly trained models.

A further improvement can be achieved by using not only an implementation for the wanted and/or noise signal model, but a plurality of models for different signal statistics. Such a system is shown by way of example inFIG. 3. Its core element is again the noise reduction algorithm20which is fed with an input signal E and which produces a corresponding output signal A. It is based on a plurality of wanted signal models211,212and a plurality of noise signal models221,222and223. A specially provided model evaluation unit24selects for the noise reduction algorithm20a model from the wanted signal models211,212and the noise signal models221,222and223. Model selection takes place on the basis of situation detection carried out by a situation detection unit25on the basis of the input signal E. With the aid of the situation detection algorithm, the signal model best suited to the current situation is selected. Situation detection is suitable for selecting, for example, the appropriate wanted signal models for voice or music.

There is again provided a data logging unit23which, in addition to the input signal E, also logs signals from the noise reduction algorithm20. It also optionally records data concerning the models selected, as symbolized by the dashed arrows inFIG. 3. The data logging unit23then provides, as in the example inFIG. 2, logged model parameters M, a logged quality metric Q and the logged input signal E. The model parameters M are used to modify the wanted signal models211,212and/or the noise signal models221,222and223.

The data provided by the data logging unit23can be used e.g. by a hearing aid acoustician to change the per se static models211,212,221,222and223during operation. This means the hearing aid acoustician can change the models e.g. using the logged model parameters M and the logged quality metric Q, as indicated by the arrow26inFIG. 3. During operation the models are again static.

The models newly trained using data logging can then, depending on the available memory, be added to the existing data models or existing models can be exchanged. Exchanging a model is indicated if the associated quality metrics Q are poor or rarely used.

In the exemplary embodiments inFIG. 2andFIG. 3the wanted signal and noise signal models are static during operation. In the example inFIG. 4dynamic models are also used. The core element of this system is once again the model-based noise reduction algorithm30to which the input signal E is fed, and from which a corresponding output signal A with reduced interfering noise can be obtained. Here the noise algorithm30is based not only on a static wanted signal model31and a static noise signal model32, but also on an updatable i.e. dynamic wanted signal model37and a likewise updatable, dynamic noise signal model38. The two dynamic models are automatically trainable by a training algorithm39. The latter derives training information from the input signal E and obtains additional situation data from the situation detector35which is likewise fed by the input signal E. On the basis of predefined criteria, possibly feedback from the noise reduction algorithm30, the model evaluation unit34makes a selection of the models to be used.

The system shown inFIG. 4operates as follows: it is basically possible to adapt the signal models37,38automatically to the signal statistic currently present. For this purpose, depending on the situation detected in the situation detection unit35, at least one new wanted or noise signal model adapted to the individual signal statistic is trained. This ongoing training generally provides continuously modified signal models. If the quality metric from model-based noise reduction30deteriorates and a sufficiently stable signal statistic is available in the new adapted signal model, the currently used signal models can be replaced by the newly trained signal models or supplemented by said new signal models.

However, the decision to exchange a signal model for a newly trained signal model can also be left to the user. To this end, as described above, automatic pre-selection of the new models is performed by means of continuous training, and the user can then switch between two combinations of effective signal models e.g. by interaction via a remote control. The better combination for the user in the current situation is then selected.

The parameters of the above described signal models are obtained by means of a training algorithm. According to another exemplary embodiment, the signal models can also be augmented by appropriate model parameters from an estimation carried out in real time. This means that the model parameters can be adapted by estimation instead of or in addition to training. To estimate the noise signal, it is possible to use, for example, the minimum statistics method or the residual noise at the output of a directional microphone signal processing unit. The parameters from the continuous training are provided with a hypothesis for the corresponding signal model by the estimated parameters. It is additionally possible, depending on the current situation, also to combine a plurality of signal models for describing a complex signal statistic instead of selecting an individual signal model, thereby enabling e.g. a plurality of noise sources with different signal statistics to be described.