Audio Inpainting

Abstract

We propose the audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their location is assumed to be known. The signal is decomposed into overlapping time-domain frames and the restoration problem is then formulated as an inverse problem per audio frame. Sparse representation modeling is employed per frame, and each inverse problem is solved using the Orthogonal Matching Pursuit algorithm together with a discrete cosine or a Gabor dictionary. The Signal-to-Noise Ratio performance of this algorithm is shown to be comparable or better than state-of-the-art methods when blocks of samples of variable durations are missing. We also demonstrate that the size of the block of missing samples, rather than the overall number of missing samples, is a crucial parameter for high quality signal restoration. We further introduce a constrained Matching Pursuit approach for the special case of audio declipping that exploits the sign pattern of clipped audio samples and their maximal absolute value, as well as allowing the user to specify the maximum amplitude of the signal. This approach is shown to outperform state-of-the-art and commercially available methods for audio declipping in terms of Signal-to-Noise Rati

Optimizing dictionary learning parameters for solving Audio Inpainting problem

“…Sparse signal representations for inpainting problems were first used in image signal processing [7] and a few years later the Audio Inpainting algorithm was introduced in [5].…”

“…It is quite clear that for a given dictionary only a certain class of signals will admit sparse expansions. In the later sections we will be concerned with audio signals and for this specific class a number of different dictionaries has been proposed, among them Gabor and DCT systems [5].…”

“…Audio inpainting presented in [5] was performed only with static dictionaries. That was the motivation for using the K-SVD algorithm to adapt the dictionary on the observed signal and therefore improve the reconstruction of missing signals.…”

“…Methods of solving a problem called Audio Inpainting were first mentioned in [5] as an algorithm for recovering missing samples in audio signal. This framework provides segmentwise restoration of audio signals where the position of missing samples is a-priori known.…”

On Homotopy Continuation for Speech Restoration

“…The reconstruction of an audio signal with missing sampled or clipped, is a classical problem in signal processing and it was largely discussed in the specialized scientific literature, see [1][2][3].…”