Enhancementof a desired speech signal in the presence of competing or interfering speech remains an unsolved problem, as it can be hard to determine which of the speech signals is the one of interest. In this paper, we propose a multichannel noise reduction algorithm which uses the presence of the user’s own voice signal, e.g. during conversations with the target speaker, as an asset to efficiently identify interfering speech and noise. Specifically, following the typical speech pattern in natural conversations, the presence of an own voice may indicate the absence of the target speech, hence undesired speech and noise can be identified and estimated during own voice presence. In contrast to conventional noise reduction systems, the proposed noise reduction systems use the user’s own voice to identify interfering speech that otherwise could be confused with the target speech. We demonstrate the performance of the proposed noise reduction systems in a comparison against state-of-the-art noise reduction systems in terms of beamforming performance for hearing assistive devices. The results show that the proposed beamforming scheme in particular outperforms state-of-the-art methods in terms of ESTOI and PESQ in situations with a target speaker and a strong interfering speaker.

Acoustic beamforming is crucial for many applications where ex-traction of a target signal from a noisy environment is required. In order to implement practical beamformers, e.g. the multichannel Wiener filter (MWF), estimation of the target and noise power spectral densities (PSDs), and the relative acoustic transfer functions (RATFs) is essential. Several methods, e.g. the so-called covariance whitening (CW) approach, have been proposed for estimating these parameters. However, it seems largely unknown that the CW approach in fact leads to maximum likelihood (ML) estimates of the RATFs. We use historical results to derive joint ML estimates (MLEs) of the RATFs and PSDs in the context of acoustic beam-forming. In addition, based on the MLEs, we propose a basic VAD framework using concentrated likelihood ratios. We use the joint MLEs of the PSDs, RATFs, and the proposed VAD to implement beamformers in a hearing aid application, and compare its performance to competing methods. Simulation results show that the pro-posed scheme can outperform competing methods, in particular in realistic situations where highly accurate prior RATF knowledge is not available or at higher signal-to-noise ratios.

In headset and hearing aid applications, it is of interest to retrieve the user's own voice in a noisy environment, e.g. for telephony applications. To do so, the cross-power spectral density (CPSD) of the interference-plus-noise is required. In this paper, a novel maximum likelihood (ML) estimator of the interference-plus-noise CPSD matrix is proposed. The proposed method is able to estimate the interference-plus-noise CPSD matrix, even during signal regions with own voice activity. The method uses a novel procedure for estimating the interference-plus-noise CPSD matrix by first estimating the interference PSD and afterwards the noise PSD in a maximum likelihood sense. Simulation experiments, where the proposed method is compared to other noise CPSD matrix estimators, show that it performs on par or better than competing methods, particularly, in situation where the interferenceto-noise ratio is large.

Speech Intelligibility Prediction (SIP) algorithms are becoming popular tools within the development and operation of speech processing devices and algorithms. However, many SIP algorithms require knowledge of the underlying clean speech; a signal that is often not available in real-world applications. This has led to increased interest in nonintrusive SIP algorithms, which do not require clean speech to make predictions. In this paper, we investigate the use of Convolutional Neural Networks (CNNs) for nonintrusive SIP. To do so, we utilize a CNN architecture that shows similarities to existing SIP algorithms, in terms of computational structure, and which allows for easy and meaningful visualization and interpretation of trained weights. We evaluate this architecture using a large dataset obtained by combining datasets from the literature. The proposed method shows high prediction performance when compared with four existing intrusive and nonintrusive SIP algorithms. This demonstrates the potential of deep learning for speech intelligibility prediction.

We propose a non-intrusive intelligibility measure for noisy and non-linearly processed speech, i.e. a measure which can predict intelligibility from a degraded speech signal without requiring a clean reference signal. The proposed measure is based on the Short-Time Objective Intelligibility (STOI) measure. In particular, the non-intrusive STOI measure estimates clean signal amplitude envelopes from the degraded signal. Subsequently, the STOI measure is evaluated by use of the envelopes of the degraded signal and the estimated clean envelopes. The performance of the proposed measure is evaluated on a dataset including speech in different noise types, processed with binary masks. The measure is shown to predict intelligibility well in all tested conditions, with the exception of those including a single competing speaker. While the measure does not perform as well as the original (intrusive) STOI measure, it is shown to outperform existing non-intrusive measures.

Speech intelligibility prediction methods are popular tools within the speech processing community for objective evaluation of speech intelligibility of e.g.enhanced speech.The Short-Time Objective Intelligibility (STOI) measure has become highly used due to its simplicity and high prediction accuracy.In this paper we investigate the use of Band Importance Functions (BIFs) in the STOI measure, i.e. of unequally weighting the contribution of speech information from each frequency band.We do so by fitting BIFs to several datasets of measured intelligibility, and cross evaluating the prediction performance.Our findings indicate that it is possible to improve prediction performance in specific situations.However, it has not been possible to find BIFs which systematically improve prediction performance beyond the data used for fitting.In other words, we find no evidence that the performance of the STOI measure can be improved considerably by extending it with a non-uniform BIF.

We propose and evaluate a binaural speech intelligibility measure. The measure is a binaural extension of the Short-Time Objective Intelligibility (STOI) measure and focuses on predicting the intelligibility of noisy speech which has been enhanced by a speech processing algorithm (e.g. in a hearing aid). We show that the measure can accurately predict 1) the Speech Reception Threshold (SRT) for a frontal speaker masked by a point noise source in the horizontal plane, 2) the improvement in SRT obtained by independently processing the left and right ear signals with Ideal Time Frequency Segregation (ITFS), and 3) the intelligibility of speech in the presence of multiple interferers as well as the effect of processing the noisy signals with 2-microphone MVDR beamforming as used in hearing aids. Finally, we show that the computational demands associated with the measure are favourable in comparison with those of a previously proposed measure with similar properties.

Objective speech intelligibility measures are gaining popularity in the development of speech enhancement algorithms and speech processing devices such as hearing aids. Such devices may process the input signals nonlinearly and modify the binaural cues presented to the user. We propose a method for predicting the intelligibility of noisy and nonlinearly processed binaural speech. This prediction is based on the noisy and processed signal as well as a clean speech reference signal. The method is obtained by extending a modified version of the short-time objective intelligibility (STOI) measure with a modified equalization-cancellation (EC) stage. We evaluate the performance of the method by comparing the predictions with measured intelligibility from four listening experiments. These comparisons indicate that the proposed measure can provide accurate predictions of (1) the intelligibility of diotic speech with an accuracy similar to that of the original STOI measure, (2) speech reception thresholds (SRTs) in conditions with a frontal target speaker and a single interferer in the horizontal plane, (3) SRTs in conditions with a frontal target and a single interferer when ideal time frequency segregation (ITFS) is applied to the left and right ears separately, and (4) the advantage of two-microphone beamforming as applied in state-of-the-art hearing aids. A MATLAB implementation of the proposed measure is available online1.

This work focuses on an adaptive beamformer in a hearing aid application using a generalized sidelobe canceller structure (GSC). In this application, the constraint and blocking matrices in the GSC structure are specifically designed using an estimate of the transfer functions between the target source and the microphones to ensure optimal beamformer performance. We show that, in practice, the GSC always-unintentionally-attenuates the target sound in a special but realistic situation where all signals, including the target and noise signals, originate from the look direction reflected by the look vector. This happens because, in practice, the blocking matrix in the GSC structure is non-ideal. We introduce a simple modification to the GSC structure, which solves the problem of undesired target signal attenuation in situations where all signals originate from the look direction. Furthermore, this modification can also prevent desired signals, originating from positions spatially close to the look direction, to be removed. We also show that the solution has no impact on other acoustic situations.

Objective intelligibility measures are increasingly being used to assess the performance of speech processing algorithms, e.g. for hearing aids. It has been shown that the short time objective intelligibility (STOI) measure yields good results in this respect. In this paper we propose a binaural extension of the STOI measure, which predicts binaural advantage using a modified equalization cancellation (EC) stage. The proposed method is evaluated for a range of acoustic conditions. Firstly, the method is able to predict the advantage of spatial separation between a speech target and a speech shaped noise (SSN) interferer. Secondly, the method yields results comparable to the monaural STOI measure when presented with noisy speech processed by ideal time-frequency segregation (ITFS). Finally, the method also performs well when presented with a selection of different acoustic conditions combined with beamforming as used in hearing aids.

This paper presents a method for least squares design of nonuniform filter banks for application in subband signal processing. Design objectives aim to optimize the filter bank frequency response while minimizing subband and output aliasing. Aliasing is minimized although magnitude and phase changes affect the aliasing terms. Filter banks with increasing bandwidth are designed with the proposed method and evaluated in speech enhancement using a spectral subtraction algorithm. When using a nonuniform frequency resolution approximating that of the human auditory system it is shown that an increased noise reduction and SNR improvement is achieved while maintaining the speech quality for a fixed number of frequency-bands.

This paper presents a new method for the design of oversampled uniform DFT-filter banks for the special application of subband adaptive beamforming with microphone arrays. Since array applications rely on the fact that different source positions give rise to different signal delays, a beamformer alters the phase information of the signals. This in turn leads to signal degradations when perfect reconstruction filter banks are used for the subband decomposition and reconstruction. The objective of the filter bank design is to minimize the magnitude of all aliasing components individually, such that aliasing distortion is minimized although phase alterations occur in the subbands. The proposed method is evaluated in a car hands-free mobile telephony environment and the results show that the proposed method offers better performance regarding suppression levels of disturbing signals and much less distortion to the source speech.

This paper presents a method for the design of nonuniform DFT filter banks for subband beamforming. Filter banks designed with the method are evaluated in subband beamforming in a real-world microphone array application. Different source positions in array applications give rise to different signal delays, which means that adaptive beamformers in the subbands alter the phase information of the subband signals in order to extract the source from a noisy background. Phase alterations in the subbands lead to signal degradations when perfect reconstruction filter banks are used for the subband decomposition and reconstruction. The objective of the proposed design is to minimize the magnitude of all aliasing components individually, such that aliasing distortion is minimized although phase alterations occur in the subbands. The proposed method is evaluated in a car hands-free mobile telephony environment with real speech signals and the results show that the performance can be increased by several decibel when using nonuniform filter banks instead of uniform filterbanks while maintaining the length of the subband filters.

Subband adaptive filters have been proposed to avoid the drawbacks of slow convergence and high computational complexity associated with time domain adaptive filters. Subband processing introduces transmission delays caused by the filter bank and signal degradations due to aliasing effects. One efficient way to reduce the aliasing effects is to allow a higher sample rate than critically needed in the subbands and thus reduce subband signal degradation. We suggest a design method, for uniform DFT filter banks with any oversampling factor, where the total filter bank group delay may be specified, and where the aliasing and magnitude/phase distortions are minimized.