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April 24, 2026The Journal of the Acoustical Society of America0 citations

Active acoustic enhancement systems: A review

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WCWill J. CassidyUniversity of SurreyGBGian Marco De BortoliAalto UniversityKPKarolina PrawdaInternational Audio Laboratories Erlangen

Key Points

  • This paper aims to provide a comprehensive overview of active acoustic enhancement systems and their functionalities.
  • Discussion of current literature on AAESs
  • Simulation of common AAES conditions
  • Analysis of system stability and topologies
  • Defined a general model for AAES as linear, time-invariant systems
  • Discussed three main topologies: in-line, regenerative, and hybrid
  • Identified promising future research directions in machine learning and artefact perception

Abstract

Active acoustic enhancement systems (AAESs) use microphones, loudspeakers and electronic processing to modify the reverberation of a space, offering flexible and cost-effective alternatives to passive variable acoustics. These systems can extend the reverberation time of a space and modify perceived characteristics such as wall distance, diffuseness and intimacy. In this article, the current literature is discussed, and common conditions of AAESs are demonstrated using simulations to help researchers to establish a comprehensive understanding of the field. A general model is first defined to approximate any AAES as a linear, time-invariant system of transfer functions. This is used to analyse the general stability condition, which is valuable for system tuning and prediction. The three main topologies of AAESs are presented, namely, in-line, regenerative and hybrid systems, describing the fundamental differences as well as the nuances of commercial implementations with a focus on signal processing techniques. Articles investigating AAESs have been summarised to allow readers to gauge the coverage of experimental research to date. The simulated contribution serves as an exploratory environment to compare AAES conditions, where code and audio examples are available online. Promising future trajectories are identified involving machine learning, artefact perception and expressive performance.

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Cite This Study

Cassidy et al. (2026) studied this question.

synapsesocial.com/papers/69eb0b25553a5433e34b4f2bhttps://doi.org/10.1121/10.0043585
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