Abstract
In this paper, an adaptive averaging a priori SNR estimation employing critical band processing is proposed. The proposed method modifies the current decision-directed a priori SNR estimation to achieve faster tracking when SNR changes. The decision-directed estimator (DD) employs a fixed weighting with the value close to one, which makes it slow in following the onsets of speech utterances. The proposed SNR estimator provides a means to solve this issue by employing an adaptive weighting factor. This allows an improved tracking of onset changes in the speech signal. As a consequence, it results in better preservation of speech components. This adaptive technique ensures that the weighting between the modified decision-directed a priori estimate and the maximum likelihood a priori estimate is a function of the speech absence probability. The estimate of the speech absence probability is modeled by a sigmoid function. Furthermore, a critical band mapping for the short-time Fourier transform analysis-synthesis system is utilized in the speech enhancement to achieve less musical noise. In addition, to evaluate the ability of the a priori SNR estimation method in preserving speech components, we proposed a modified objective measurement known as modified hamming distance. Evaluations are performed by utilizing both objective and subjective measurements. The experimental results show that the proposed method improves the speech quality under different noise conditions. Moreover, it maintains the advantage of the DD approach in eliminating the musical noise under different SNR conditions. The objective results are supported by subjective listening tests using 10 subjects (5 males and 5 females).
Highlights
Noise suppression and speech enhancement are essential techniques employed in many products, for instance, mobile phones, hearing aids, and assistive listening devices
6.6 Evaluate the benefit of the critical band processing In order to demonstrate the benefit of using critical band processing as a preprocessor to the speech enhancement framework, we present a comparison of objective measurement before and after applying the critical band processing with the proposed a priori signal to noise ratio (SNR) estimation method combined with different gain functions
As a basis for the adaptation, the a priori SNR estimation employs a model of speech absence probability based on a sigmoid function
Summary
Noise suppression and speech enhancement are essential techniques employed in many products, for instance, mobile phones, hearing aids, and assistive listening devices. The sigmoid consists of two parameters, σ to control transition speed and ρ to determine the threshold of active speech signal and noise [32] The selection of these parameter values is based on the observation that the a priori SNR equals the posterior SNR for high SNRs. An adaptive weighting function β(i, m) is proposed based on the a posteriori SNR and is given by β(i, m) β0 exp[ −σ (γ (i, m) ρ)]. The resulting weighting factor β(i, m) is close to 1, which means that the proposed method will have identical behavior as the DD and the MDD methods This explains the ability of the proposed method to maintain the advantage of the DD method in reducing musical noise in the low SNRs. Since the second term is zero, the a priori SNR estimate in noise frames will be given by ξp↓rop(i, m) = β(i, m)GC2 B(i, m − 1)γ (i, m). The apparent result is that more speech components are preserved as well as a reduction in the speech transient distortion
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