Audio Engineering Society 123rd Convention Oct 58, 2007 Alexey Lukin and Jeremy Todd presented a paper "Suppression of Musical Noise Artifacts in Audio Noise Reduction by Adaptive 2D Filtering". It discusses suppression of objectionalble musical noise artifacts resulting from the spectral subtraction method for broadband noise reduction. The idea of the method is to use a combination of NonLocal Means and DFT Thresholding algorithms to smooth the 2D map of SNR ratios in spectral subtraction. AbstractSpectral attenuation algorithms for audio noise reduction often generate annoying musical noise artifacts. Most existing methods for suppression of musical noise employ a combination of instantaneous and timesmoothed spectral estimates for calculation of spectral gains. In this paper, a 2D approach to the filtering of a timefrequency spectrum is proposed, based on a recently developed NonLocal Means image denoising algorithm. The proposed algorithm demonstrates efficient reduction of musical noise, without creating "noise echoes" artifacts inherent in timesmoothing methods. To purchase copies of the paper itself, please see the AES website. The full text can also be downloaded here: LukinTodd07.pdf. PowerPoint PresentationYou can download our PowerPoint presentation given at AES here. Download PowerPoint Slide Show for Windows: LukinTodd_AES123.pps (690 KB) Audio Examples and SpectrogramsHere we provide audio examples comparing the algorithms mentioned in our paper. Here's the WAV file with a fragment of the audio corrupted by white noise. Figure 1. Spectrogram of noisy audio To clean the audio, a spectral subtraction method is typically used, but it can produce "musical noise" artifacts manifesting themselves as spurious bursts of energy in random places of the audio spectrum. The results are given in a WAV file whose spectrogram is shown below in Figure 2. Figure 2. Spectrogram of the denoised audio showing musical noise artifacts The most common way to reduce the musical noise artifact is EphraimMalah method that uses "apriori" (timesmoothed) and "aposteriori" (instantaneous) energy estimates to compute the gain function for spectral subtraction. The musical noise is effectively smoothed out, but at the expense of some smearing of transients and "noisy tails"  regions of unsuppressed noise after abrupt signal fades. The results are given in a WAV file whose spectrogram is shown below in Figure 3. Figure 3. Spectrogram of the audio denoised by the EphraimMalah method In the paper, we are showing how these problems of EphraimMalah method can be addressed by the use of a recently proposed NonLocal Means algorithm for image denoising. Our results are given in a WAV file whose spectrogram is shown below in Figure 4. Figure 4. Spectrogram of the audio denoised by the proposed method Demo ApplicationWe have implemented the proposed noise reduction algorithm in iZotope RX restoration application. You can open an audio file, train the noise profile, and apply Denoiser to perform the noise reduction. In the Advanced panel, the "MNS algo" combo box controls the algorithm of musical noise suppression. Select "Advanced" for the NonLocal Means algorithm, "Extreme" for DFTT algorithm, or "Adv+Extreme" for NLM+DFTT algorithm. Download iZotope RX Demo for Windows or OS X.
