Resound is an AI audio editor for podcasters that automatically removes filler words, long silences and mouth sounds in one click.
Resound was built to solve the most tedious hour of podcast production: manually scrubbing out “um”s, awkward pauses and pops. Upload an episode and its machine-learning models detect filler words, silences and mouth noises, then propose an edit session you can review before anything is cut. The multitrack editor handles music beds, intro/outro assembly and loudness-ready export, so a finished episode can go from raw recording to publishable file without opening a traditional DAW. Because edits are suggested rather than forced, hosts keep control over pacing and comedic beats.
A common workflow is to record the raw episode, upload it before lunch, and review Resound’s suggested cuts while listening back once. Editors toggle individual suggestions β keeping intentional pauses and comedic beats β then assemble intro, ads and outro on the multitrack timeline. The export step normalizes loudness to platform standards, so the file that leaves Resound is ready for RSS distribution without a mastering pass.
Resound offers a free trial with limited processing hours, then paid plans billed by monthly processing time (with higher tiers for teams and networks). Longer episodes and batch processing are available on upper tiers.
It is made for podcasters, interview show hosts and audio content teams who edit their own episodes and want to cut post-production time dramatically without sacrificing editorial control.
A free trial lets you process a limited amount of audio; ongoing use requires a paid plan based on monthly processing hours.
Resound is primarily a studio product and does not market a public API; automation is handled through its batch processing features.
Where Descript offers a broad text-based editor, Resound focuses narrowly on automated audio cleanup quality and speed, making it a lighter-weight companion or replacement for manual DAW editing.
Resound accepts common recording formats such as WAV and MP3 for import and exports finished episodes to standard audio files, with video podcast workflows supported on higher tiers.
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