L-ISA Hyperreal Sound: optimizing coverage and spatial rendering for fill systems
For loudspeaker systems with limited shared coverage (distributed fill systems like front-fills or under-balcony fills), the L-ISA technology offers the option of creating “spatial-fills”. The approach first creates virtual replicates of the scene system loudspeakers to restore cross-coverage and then uses gain-based algorithm for positioning audio objects. This improves audio object separation and audio-visual consistency while assuring coverage and level consistency.
L-ISA Hyperreal Sound: Why amplitude-based algorithms are best suited to frontal loudspeaker configurations?
L-ISA technology enables the sound of a performer to be perceived as coming from their location on stage and creates natural separation of the multiple sounds. This is allowed by a combination of loudspeaker system design recommendations and a custom amplitude-based panning algorithm. In this white paper, we show the L-ISA algorithm generally outperforms delay-based algorithms for any type of shows, from soft natural voice lifting to high SPL performances.
Measurement quality at high frequencies
The frequency response of a loudspeaker system can vary over time due to changing atmospheric conditions such as:
- Temperature and humidity (slow variations),
- Wind (fast variations).
Measurement quality at low frequencies
Background noise affects the quality of measured frequency responses of a loudspeaker system. Repeated measurements can reveal very different results, especially at low frequencies, compromising optimal system tuning decisions (EQ, quality of summation for time alignment, etc.).Combination of multiple acquisitions with appropriate test signal parameters can help getting more consistent and qualitative measurements at low frequencies.
Optimum measurement locations for loudspeaker system equalization
A loudspeaker system tuning ideally aims at optimizing the whole audience area. However, onsite EQ choices must be based on a limited number of measured frequency responses. To avoid EQ mistakes, the key is to capture a representative set of measurements. It should:
- Smooth spatial variations in the average frequency response,
- Reveal spatially stable patterns in the individual frequency responses.