In this paper, we present a new approach for noise reduction. A binary time-frequency (T-F) masking threshold criterion is proposed and analyzed with respect to the average spectra of music and noise disturbances. Modified autoregressive (AR) detection and AR interpolation are then applied to the residual signal of the binary masking process. The proposed method is able to reduce supergaussian and impulsive noise while ensuring preservation of the desired signal, which is crucial for professional high-quality audio restoration, and it is also suitable for Gaussian noise to a certain extent. The approach is compared to a state-of-the-art restoration algorithm by means of the objective measures signal-to-noise ratio (SNR) improvement and perceptual quality, and by subjective listening tests. The objective results as well as the listening tests show that the proposed algorithm is especially suited for supergaussian, grainy-sounding noise types, e.g., optical soundtrack noise of celluloid movie footage, or rain noise.