segments and enable second-scale gap filling by substituting the most suitable segment for the gap.

Though these methods have shown quite successes at multiple gap scales, to the best of our knowledge, none have tailored for audio editing, where user could mask out an unwanted segment of an audio, expecting the restoration to sound natural and meaningful (in cases of speech). While audio editing could be utilized to a broad range of applications, such as removal of environmental noises in a speech or removal of human sound during bird sound recordings, we show current algorithms that targets at second-scale gaps, such as [@perraudin2018inpainting] fails when applied onto the scenario[^1] (cf. Table \[tab:benchmark\]).

Long ($>$ 200 ms) audio inpainting for editing is a very challenging task. Firstly, gaps are commonly at the scale of seconds, rendering the algorithms for shorter gaps in vain. Secondly, in cases of speech, signals are mostly aperiodic and thereby invalidating example based methods such as [@perraudin2018inpainting]. Thirdly, data are in high dimension ($>$16k/sec) and the correlation between neighboring samples is rather low and thus directly applying state-of-the-art models in image or video inpainting tends not to work well.

Also, while image inpainting has been extensively and 