In environments such as open offices, call centres, etc., speech is often the main disturbing source of ambient noise, reducing concentration and productivity. Active noise control (ANC) systems have difficulties in dealing with speech due to its non-stationary nature and constraints in the ANC system, which require the optimal filters to be non-causal. The non-causality is due to the delay incurred by, e.g., digital processing or acoustic propagation paths. To deal with this, we propose a new feedforward ANC system for headphone applications, HMD-ANC, which improves voiced speech attenuation. Notably, in HMD-ANC, each speech harmonic and its quadrature version, obtained by the harmonic model decomposition, are predicted by a two-weight adaptive filter to overcome the delay. The results show that HMD-ANC outperforms conventional adaptive feedforward ANC for delays starting from 6 samples (0.125 ms) at a sampling frequency of 48 kHz. Moreover, HMD-ANC extends the attenuation bandwidth, e.g., up to 2.5 kHz, while the conventional ANC is limited to 1 kHz.

In some situations, such as open office spaces, speech can play the role of an unwanted and disturbing source of noise, and ANC headphones or earbuds might help to solve this problem. However, ANC in modern headphones is often based on a pre-calculated fixed-filter for practical reasons, like stability and cost. Moreover, in some cases the optimal filter is non-causal, which cannot be realized with such a filter, and ANC attenuation performance will be significantly decreased. In this paper we propose to solve the causality problem in feedforward fixed-filter ANC systems by integrating a long-term linear prediction filter to predict the incoming disturbance, here speech, by the same amount of samples ahead in time, as the non-causal delay. The proposed ANC system outperforms conventional adaptive feedforward ANC systems in terms of computational complexity, showing comparable or better results on voiced speech attenuation at non-causal delays from 4 to 18 samples (0.5 to 2.25 ms) at a sampling frequency of 8 kHz.

In this paper, single channel speech enhancement in the time domain is considered. We address the problem of modelling non-stationary speech by describing the voiced speech parts by a harmonic linear chirp model instead of using the traditional harmonic model. This means that the speech signal is not assumed stationary, instead the fundamental frequency can vary linearly within each frame. The linearly constrained minimum variance (LCMV) filter and the amplitude and phase estimation (APES) filter are derived in this framework and compared to the harmonic versions of the same filters. It is shown through simulations on synthetic and speech signals, that the chirp versions of the filters perform better than their harmonic counterparts in terms of output signal-to-noise ratio (SNR) and signal reduction factor. For synthetic signals, the output SNR for the harmonic chirp APES based filter is increased 3 dB compared to the harmonic APES based filter at an input SNR of 10 dB, and at the same time the signal reduction factor is decreased. For speech signals, the increase is 1.5 dB along with a decrease in the signal reduction factor of 0.7. As an implicit part of the APES filter, a noise covariance matrix estimate is obtained. We suggest using this estimate in combination with other filters such as the Wiener filter. The performance of the Wiener filter and LCMV filter are compared using the APES noise covariance matrix estimate and a power spectral density (PSD) based noise covariance matrix estimate. It is shown that the APES covariance matrix works well in combination with the Wiener filter, and the PSD based covariance matrix works well in combination with the LCMV filter.

In speech processing, the speech is often considered stationary within segments of 20-30 ms even though it is well known not to be true. In this paper, we take the nonstationarity of voiced speech into account by using a linear chirp model to describe the speech signal. We propose a maximum likelihood estimator of the fundamental frequency and chirp rate of this model, and show that it reaches the Cramer-Rao lower bound. Since the speech varies over time, a fixed segment length is not optimal, and we propose making a segmentation of the signal based on the maximum a posteriori criterion. Using this segmentation method, the segments are on average longer for the chirp model compared to the traditional harmonic model. For the signal under test, the average segment length is 24.4 and 17.1 ms for the chirp model and traditional harmonic model, respectively. This suggests a better fit of the chirp model than the harmonic model to the speech signal. The methods are based on an assumption of white Gaussian noise, and, therefore, two prewhitening filters are also proposed.

In this paper, the issue of single channel speech enhancement of non-stationary voiced speech is addressed. The non-stationarity of speech is well known, but state of the art speech enhancement methods assume stationarity within frames of 20–30 ms. We derive optimal distortionless filters that take the non-stationarity nature of voiced speech into account via linear constraints. This is facilitated by imposing a harmonic chirp model on the speech signal. As an implicit part of the filter design, the noise statistics are also estimated based on the observed signal and parameters of the harmonic chirp model. Simulations on real speech show that the chirp based filters perform better than their harmonic counterparts. Further, it is seen that the gain of using the chirp model increases when the estimated chirp parameter is big corresponding to periods in the signal where the instantaneous fundamental frequency changes fast.

In this paper, we consider single-channel speech enhancement in the short time Fourier transform (STFT) domain. We suggest to improve an STFT phase estimate by estimating the initial phases. The method is based on the harmonic model and a model for the phase evolution over time. The initial phases are estimated by setting up a least squares problem between the noisy phase and the model for phase evolution. Simulations on synthetic and speech signals show a decreased error on the phase when an estimate of the initial phase is included compared to using the noisy phase as an initialisation. The error on the phase is decreased at input SNRs from -10 to 10 dB. Reconstructing the signal using the clean amplitude, the mean squared error is decreased and the PESQ score is increased.

A new filter design based on joint diagonalization of the clean speech and noise covariance matrices is proposed. First, an estimate of the noise is found by filtering the observed signal. The filter for this is generated by a weighted sum of the eigenvectors from the joint diagonalization. Second, an estimate of the desired signal is found by subtraction of the noise estimate from the observed signal. The filter can be designed to obtain a desired trade-off between noise reduction and signal distortion, depending on the number of eigenvectors included in the filter design. This is explored through simulations using a speech signal corrupted by car noise, and the results confirm that the output signal-to-noise ratio and speech distortion index both increase when more eigenvectors are included in the filter design.