Different noise-reduction methods have been proposed in the literature for single and multiple-microphone applications. For binaural hearing aids, multiple-microphone noise-reduction methods offer two significant psycho-acoustical advantages with respect to the single-microphone methods. First, noise reduction strategies based on binaural processing (processing using the information received at the left and right ear) are more effective than using independent monaural processing because of the added information. Second, there is an user preference for noise reduction methods that preserve localization cues of both target and interfering signals. Although different multiple-microphone noise-reduction techniques have been proposed in the literature, only a small set is able to preserve the localization cues for both target and interfering signals. This paper proposes a binaural noise-reduction method that preserves the localization cues for both target and interfering signals. The proposed method is based on blind source separation (BSS) followed by a postprocessing technique inspired by a human auditory model. The performance of the proposed method is analyzed using objective and subjective measurements, and compared to existing binaural noise-reduction methods based on BSS and multichannel Wiener filter (MWF). Results show that for some scenarios and conditions, the proposed method outperforms on average the existing methods in terms of noise reduction and provides nearly similar sound quality.

Most speech enhancement applications perform frequency shaping by means of multiplication in the frequency domain. Operating in the frequency domain is equivalent to convolution in the time domain. In these speech enhancement algorithms, the updating of frequency response alone cannot ensure the fulfillment of the conditions required for multiplication in frequency to correspond to linear convolution instead of circular convolution. As a result, artifacts and distortions may be present in the output of a standard fast Fourier transform (FFT)-based algorithm. Typical methods to deal with these artifacts involve overlapping and windowing. However, even using these strategies, artifacts may be perceptually noticeable under certain signal-to-noise ratio (SNR) conditions and/or when a high sampling frequency is employed. This paper analyzes the efficiency of the standard methods, explains the source of these distortions, provides a perceptual evidence of these artifacts, and proposes two alternative methods to perform artifact-free and distortion-free FFT convolution. These methods are based on the extension of the impulse response and the splitting of the impulse response in two impulse responses, operations that are performed in the frequency-domain. Computational costs and performance of the proposed techniques are also discussed.

In most speech enhancement algorithms, a frequency response is computed per each block, and a multiplication in the frequency domain is used to apply this frequency response. Typically, in these algorithms, the updating procedure cannot ensure the fulfillment of the condition to perform a linear convolution instead of a circular convolution. As a result, artifacts and distortions may be present. Standard methods to deal with these distortions use overlapping and windowing. This work presents a detailed analysis of these distortions and proposes a new method to perform a artifact-free and distortion-free convolution in the FFT domain.