Audio inpainting (also known as audio interpolation) is an audio restoration task which deals with the reconstruction of missing or corrupted portions of a digital audio signal.[1] Inpainting techniques are employed when parts of the audio have been lost due to various factors such as transmission errors, data corruption or errors during recording.[2]
The goal of audio inpainting is to fill in the gaps (i.e., the missing portions) in the audio signal seamlessly, making the reconstructed portions indistinguishable from the original content and avoiding the introduction of audible distortions or alterations.[3]
Many techniques have been proposed to solve the audio inpainting problem and this is usually achieved by analyzing the temporal[1][4][5] and spectral[3][2] information surrounding each missing portion of the considered audio signal.
Corrupted spectrogram (top) and its reconstruction after performing audio inpainting (bottom)
Classic methods employ statistical models or digital signal processing algorithms [1][4][5] to predict and synthesize the missing or damaged sections. Recent solutions, instead, take advantage of deep learning models, thanks to the growing trend of exploiting data-driven methods in the context of audio restoration.[3][2][6]
Depending on the extent of the lost information, the inpaintining task can be divided in three categories.
Short inpainting refers to the reconstruction of few milliseconds (approximately less than 10) of missing signal, that occurs in the case of short distortions such as clicks or clipping.[7]
In this case, the goal of the reconstruction is to recover the lost information exactly.
In long inpainting instead, with gaps in the order of hundreds of milliseconds or even seconds, this goal becomes unrealistic, since restoration techniques cannot rely on local information.[8]
Therefore, besides providing a coherent reconstruction, the algorithms need to generate new information that has to be semantically compatible with the surrounding context (i.e., the audio signal surrounding the gaps).[3]
The case of medium duration gaps lays between short and long inpainting.
It refers to the reconstruction of tens of millisecond of missing data, a scale where the non-stationary characteristic of audio already becomes important.[9]
^ abcMokrý, Ondřej; Rajmic, Pavel (2020). "Audio Inpainting: Revisited and Reweighted". IEEE/ACM Transactions on Audio, Speech, and Language Processing. 28: 2906–2918. arXiv:2001.02480. doi:10.1109/TASLP.2020.3030486. S2CID 210064378.
^ abcdMarafioti, Andres; Majdak, Piotr; Holighaus, Nicki; Perraudin, Nathanael (January 2021). "GACELA: A Generative Adversarial Context Encoder for Long Audio Inpainting of Music". IEEE Journal of Selected Topics in Signal Processing. 15 (1): 120–131. arXiv:2005.05032. Bibcode:2021ISTSP..15..120M. doi:10.1109/JSTSP.2020.3037506. S2CID 218581410.
^ abAdler, Amir; Emiya, Valentin; Jafari, Maria G.; Elad, Michael; Gribonval, Rémi; Plumbley, Mark D. (March 2012). "Audio Inpainting". IEEE Transactions on Audio, Speech, and Language Processing. 20 (3): 922–932. doi:10.1109/TASL.2011.2168211. S2CID 11136245.
^ abJanssen, A.; Veldhuis, R.; Vries, L. (April 1986). "Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes" (PDF). IEEE Transactions on Acoustics, Speech, and Signal Processing. 34 (2): 317–330. doi:10.1109/TASSP.1986.1164824. S2CID 17149340.
^Cite error: The named reference caw was invoked but never defined (see the help page).
^Applications of digital signal processing to audio and acoustics (6. Pr ed.). Boston, Mass.: Kluwer. 2003. pp. 133–194. ISBN 978-0-7923-8130-3.
^Perraudin, Nathanael; Holighaus, Nicki; Majdak, Piotr; Balazs, Peter (June 2018). "Inpainting of Long Audio Segments With Similarity Graphs". IEEE/ACM Transactions on Audio, Speech, and Language Processing. 26 (6): 1083–1094. arXiv:1607.06667. doi:10.1109/TASLP.2018.2809864. S2CID 3532979.
^Marafioti, Andres; Perraudin, Nathanael; Holighaus, Nicki; Majdak, Piotr (December 2019). "A Context Encoder For Audio Inpainting". IEEE/ACM Transactions on Audio, Speech, and Language Processing. 27 (12): 2362–2372. doi:10.1109/TASLP.2019.2947232. S2CID 53102801.
Audioinpainting (also known as audio interpolation) is an audio restoration task which deals with the reconstruction of missing or corrupted portions...
approach to inpainting was established. Helmut Ruhemann (1891–1973), a German restorer and conservator, led the discussions on the use of inpainting in conservation...
subscribe monthly or annually to unlock more capabilities such as audioinpainting. Founded in December 2023 by a team of former researchers for Google...
P. Nichols Joseph M. Palmaccio Christian Zwarg Record restoration Audioinpainting Digitizing the voices of the past / Science perfects sound of century-old...
playing Spectrum Editor with various display settings and editing tools "AudioInpainting" to recreate missing content by analyzing the surrounding area Visualizers...
AI-powered updates which includes AI image generation, AI infill, AI inpainting and more. "Inmagine Group Announces Restructure into Two Independent Businesses...
various image restoration tasks, including denoising, super-resolution, and inpainting. The use of generative adversarial networks (GANs) has also gained attention...
consider image inpainting. The conditions are x ~ {\displaystyle {\tilde {x}}} , the reference image, and m {\displaystyle m} , the inpainting mask. The conditioning...
different self-supervised training techniques, including transformation, inpainting, patch discrimination and clustering. Examples of generative approaches...
films. Texture synthesis can be used to fill in holes in images (as in inpainting), create large non-repetitive background images and expand small pictures...
repository audit method based on risk assessment Historic paint analysis Inpainting Kintsugi Leafcasting Lining of paintings Mass deacidification Overpainting...
video and audio processing. Sparsity and overcomplete dictionaries have immense applications in image compression, image fusion and inpainting. Given the...
Verge. Retrieved June 13, 2020. Yu, Jiahui, et al. "Generative image inpainting with contextual attention." Proceedings of the IEEE conference on computer...
repository audit method based on risk assessment Historic paint analysis Inpainting Kintsugi Leafcasting Lining of paintings Mass deacidification Overpainting...
repository audit method based on risk assessment Historic paint analysis Inpainting Kintsugi Leafcasting Lining of paintings Mass deacidification Overpainting...
million movies, videos and TV shows, 1 million software programs, 15 million audio files, 4.7 million images, and 840 billion web pages. The courses at Maynooth...
repository audit method based on risk assessment Historic paint analysis Inpainting Kintsugi Leafcasting Lining of paintings Mass deacidification Overpainting...
repository audit method based on risk assessment Historic paint analysis Inpainting Kintsugi Leafcasting Lining of paintings Mass deacidification Overpainting...
successfully applied to inverse problems such as denoising, super-resolution, inpainting, and film colorization. These applications include learning methods such...
repository audit method based on risk assessment Historic paint analysis Inpainting Kintsugi Leafcasting Lining of paintings Mass deacidification Overpainting...
obtained compared to the simpler approaches. An example in this field is inpainting. The organization of a computer vision system is highly application-dependent...
repository audit method based on risk assessment Historic paint analysis Inpainting Kintsugi Leafcasting Lining of paintings Mass deacidification Overpainting...
repository audit method based on risk assessment Historic paint analysis Inpainting Kintsugi Leafcasting Lining of paintings Mass deacidification Overpainting...
Global Information