The quality and intelligibility of single channel speech degraded by additive noise remains a challenging problem when only the noisy speech is available. An accurate estimation of the noise spectrum is important for the effective performance of speech enhancement algorithms, especially in nonstationary noise environments. This thesis addresses both two issues. First, a speech enhancement algorithm using harmonic features is introduced. A spectral weighting function is derived by constrained optimization to suppress noise in the frequency domain. Two design parameters are included in the suppression gain, namely the frequency-dependent noise-flooring parameter (FDNFP) and the gain factor. The FDNFP controls the level of admissible residual noise in the enhanced speech, while further enhancement is achieved by adaptive comb filtering using the gain factor with a peak-picking algorithm. Second, a noise estimation algorithm is proposed for nonstationary noise conditions. The speech presence probability is updated by introducing a time-frequency dependent threshold. The frequency dependent smoothing factor for noise estimation is computed based on the estimated speech presence probability in each frequency bin. This algorithm adapts quickly to nonstationary noise environments and preserves more information on weak speech phoneme. The performance of the proposed speech enhancement algorithm is evaluated in terms of Perceptual Evaluation of Speech Quality (ITU-PESQ) scores and Modified Bark Spectral Distortion (MBSD) measures, composite objective measures and listening tests. Our listening tests indicate that 16 listeners on average preferred our harmonic enhanced speech over any of three other approaches about 73% of the time. The performance of the proposed noise estimation algorithm combined with the proposed speech enhancement method in nonstionary noise environments is also tested in terms of ITU-PESQ scores and MBSD measures. Experimental results indicate that the proposed noise estimation algorithm when integrated with the harmonic enhancement method outperforms spectral subtraction, signal subspace method, a perceptually-based enhancement method with a constant noise-flooring parameter, and our original harmonic speech enhancement method in highly nonstationary noise environments.