Designing Quasi-Linear Phase IIR Filters for Audio Crossover Systems by Using Swarm Intelligence

Foresi F, Vecchiotti P, Zallocco D, Squartini S. Designing Quasi-Linear Phase IIR Filters for Audio Crossover Systems by Using Swarm Intelligence. In: AES Convention 144. Audio Engineering Society; 2018. Paper 9992. Available from: https://aes.org/publications/elibrary-page/?id=19509

@inproceedings{Foresi2018_19509,
  author = {Foresi, Ferdinando and Vecchiotti, Paolo and Zallocco, Diego and Squartini, Stefano},
  title = {{Designing Quasi-Linear Phase IIR Filters for Audio Crossover Systems by Using Swarm Intelligence}},
  booktitle = {AES Convention 144},
  note = {Paper 9992},
  year = {2018},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=19509}
}

TY  - CPAPER
TI  - Designing Quasi-Linear Phase IIR Filters for Audio Crossover Systems by Using Swarm Intelligence
AU  - Foresi, Ferdinando
AU  - Vecchiotti, Paolo
AU  - Zallocco, Diego
AU  - Squartini, Stefano
T2  - AES Convention 144
M1  - Paper 9992
PY  - 2018
DA  - 2018/05/06
UR  - https://aes.org/publications/elibrary-page/?id=19509
PB  - Audio Engineering Society
LA  - en
AB  - In sound reproduction systems the audio crossover plays a fundamental role. Nowadays, digital crossover based on IIR filters are commonly employed, of which non-linear phase is a relevant topic. For this reason, solutions aiming to IIR filters approximating a linear phase behavior have been recently proposed. One of the latest exploits Fractional Derivative theory and uses Evolutionary Algorithms to explore the solution space in order to perform the IIR filter design: the IIR filter phase error is minimized to achieve a quasi-linear phase response. Nonetheless, this approach is not suitable for a crossover design, since the single filter transition band behavior is not predictable. This shoved the authors to propose a modified design technique including suitable constraints, as the amplitude response cut-off frequency, in the ad-hoc Particle Swarm Optimization algorithm exploring the space of IIR filter solutions. Simulations show that not only more performing filters can be obtained but also fully flat response crossovers achieved.
ER  -