Speech enhancement based on a constrained sinusoidal model

ABSTRACTAn algorithm for enhancement of speech degraded by additivebroad-bandnoise is proposed. The algorithm represents speech us-ing a sinusoidal model, where model parameters are estimated it-eratively. In order to ensure speech-like characteristics observed inclean speech, the model parameters are restricted to satisfy certainsmoothness constraints. The algorithm is evaluated using speechsignals degraded by additive white Gaussian noise. Results fromboth objective and subjective evaluations show considerable im-provement over traditional spectral subtraction and Wiener ﬁlter -ing based schemes. In particular, in a subjective AB preferencetest, where enhanced signals were encoded/decoded with the G729speech codec, the proposed scheme was preferred over the tradi-tional schemes in more than 5 out of 6 cases for input SNRs rang-ing from 5-20dB.1. INTRODUCTIONOften, there is a need for digital voice communications systems orautomatic speech recognition systems to perform reliably in noisyenvironments. In many cases, these systems work well in nearlynoise-freeconditions, while their performance deteriorates rapidlyin noisy conditions. Thus, thedevelopment of preprocessing algo-rithms for reducing the background degradation in speech signalsis of current interest.In the past, a number of single-microphonespeech enhance-ment algorithms have been proposed. These include variants ofspectral subtraction [2], methods based on all-polemodeling [5, 8],subspace based methods, e.g. [3, 7], and algorithms that exploitmasking effects, e.g. [13]. Although successful for speech coding[9] and speech modiﬁcation [12], the sinusoidal model has not re-ceived the same level of attention for speech enhancement [1, 11]. In this paper, we propose a sinusoidal model based algorithmfor enhancement of speech degraded by additive broadband noise.Adopting a similar idea as that used in [5], for an all-polemodel,the present algorithm exploits the notion that during the speechproduction process, the vocal tract transfer function varies con-tinuously and relatively slowly with time. Furthermore, in mostvoiced speech regions, the fundamental frequency varies relativelyslowly as well. The aim of the proposed enhancement scheme is toimprove the quality of the enhanced speech signal, by exploitingthis knowledge of the signals origin in the enhancement process.The remainder of the paper is structured as follows. Sec. 2introduces the signal model. Sec. 3 gives an overview of the en-hancement algorithm, followed by a more detailed treatment ofeach step of the algorithm. In Sec. 4 the algorithm is evaluated us-ing signals degraded with additive white Gaussian noise (AWGN). The evaluation is based on objective speech quality measures aswell as subjective tests. Finally, Sec. 5 concludes and outlines di-rections for future research.2. SIGNAL MODEL AND ASSUMPTIONSWe assume that the speech signal is corrupted by additive broad-band noise, as follows,where , and denote the noisy speech signal vector, the cleansignal, and the noise component, respectively. Furthermore, itis assumed that the noise is stationary, such that the noise spec-trum estimated in noise-onlyregions is still valid, when speech ispresent. The enhanced speech signal is modeled as a sum of sinu-soids on a frame-by-framebasis: