#include <filter_morph.hpp>
|
| static constexpr int | latency_samples () noexcept |
| static constexpr int | tail_samples () noexcept |
| | Recursive biquads have an asymptotic (formally unbounded) tail.
|
|
| static constexpr SampleType | min_frequency_hz = SampleType{20} |
| static constexpr SampleType | min_q = SampleType{0.1} |
| static constexpr SampleType | max_q = SampleType{20} |
| static constexpr SampleType | max_sample_rate = SampleType{384000} |
◆ configure()
template<typename SampleType = float>
Transactionally design both endpoints.
Invalid input leaves the live filters and their state untouched. Call only at a block boundary.
◆ configured()
template<typename SampleType = float>
◆ first_coefficients()
template<typename SampleType = float>
◆ first_endpoint()
template<typename SampleType = float>
◆ latency_samples()
template<typename SampleType = float>
|
|
inlinestaticconstexprnoexcept |
◆ magnitude()
template<typename SampleType = float>
Exact complex parallel sum of the two live sections.
◆ magnitude_db()
template<typename SampleType = float>
◆ morph()
template<typename SampleType = float>
◆ process()
template<typename SampleType = float>
◆ process_block() [1/2]
template<typename SampleType = float>
| bool pulp::signal::FilterMorphT< SampleType >::process_block |
( |
const SampleType * | input, |
|
|
SampleType * | output, |
|
|
std::size_t | frames ) |
|
inlinenoexcept |
◆ process_block() [2/2]
template<typename SampleType = float>
◆ reset()
template<typename SampleType = float>
◆ response_curve_db()
template<typename SampleType = float>
| void pulp::signal::FilterMorphT< SampleType >::response_curve_db |
( |
double | min_hz, |
|
|
double | max_hz, |
|
|
std::span< float > | output ) const |
|
inlinenoexcept |
◆ sample_rate()
template<typename SampleType = float>
◆ second_coefficients()
template<typename SampleType = float>
◆ second_endpoint()
template<typename SampleType = float>
◆ set_morph()
template<typename SampleType = float>
Set the linear-amplitude morph position.
A convex output blend avoids hidden midpoint gain and keeps the canonical endpoint normalizations.
◆ tail_samples()
template<typename SampleType = float>
|
|
inlinestaticconstexprnoexcept |
Recursive biquads have an asymptotic (formally unbounded) tail.
◆ max_q
template<typename SampleType = float>
◆ max_sample_rate
template<typename SampleType = float>
◆ min_frequency_hz
template<typename SampleType = float>
◆ min_q
template<typename SampleType = float>
The documentation for this class was generated from the following file: