Barry, DanDanBarryRagano, AlessandroAlessandroRaganoHines, AndrewAndrewHines2024-05-082024-05-082020 IEEE2020-09-24http://hdl.handle.net/10197/25883The IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP 2020), Tempere, Finland, 21-24 September 2020Sound source separation algorithms have advanced significantly in recent years but many algorithms can suffer from objectionable artefacts. The artefacts include phasiness, transient smearing, high frequency loss, unnatural sounding noise floor and reverberation to name a few. One of the main reasons for this is due to the fact that in many algorithm, individual time-frequency bins are often only attributed to one source at a time, meaning that many time-frequency bins will be set to zero for a separated source. This leads to an impressive signal to interference ratio but at the cost of natural sounding resynthesis. Here, we present a simple algorithm capable of audio inpainting based on selfsimilarity within the signal. The algorithm attempts to use the non-zero bin values observed in similar frames as substitutes for the zero bin values in the current analysis frame. We present results from subjective listening tests which show a preference for the inpainted audio over the original audio produced from a simple source separation algorithm. Further, we use the Fr´echet Audio Distance metric to evaluate the perceptual effect of the proposed inpainting algorithm. The results of this evaluation support the subjective test preferences.en© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.AudioSource separationInpaintingSelf similarityAudio Inpainting based on Self-similarity for Sound Source Separation ApplicationsConference Publication10.1109/MMSP48831.2020.92871042020-08-1312/RC/2289-P213/RC/2077https://creativecommons.org/licenses/by-nc-nd/3.0/ie/