A Content-Aware Time Compression Algorithm for Audio - CATCA

Abstract

Modern audio time-compression algorithms generally follow a uniform approach to speedup. Given a particular playback rate, these algorithms decrease the number of audio samples played evenly throughout the entire clip and use a variety of techniques to control the pitch so that it remains constant. This is generally e!ective until higher speeds, past which the quality of the audio degrades to a point of lacking comprehensibility to the listener. However, by designing an algorithm that analyzes the frequencies in each audio sample and removes them strategically according to their perceived importance, it is theoretically possible to preserve the intelligibility of an audio file better even at higher playback rates. This unlocks potentially higher speeds for listener comprehension and improves the listening experience at standard playback rates. This algorithm, called CATCA (Content-Aware Time Compression for Audio), is built on a content-aware approach, which assigns energies to audio samples and removes them in priority of lowest energy. While this new time-compression algorithm did not achieve intelligibility improvements over the state-of-the-art method PSOLA, it still performed better than other algorithm variations, demonstrating the utility of content-awareness as an audio time-compression approach approach given future improvements.