Synthesis of the NTF of Delta Sigma modulators (pydsm.NTFdesign)

This module provides some strategies for the design of the Noise Transfer Function of ΔΣ modulators. There are both functions that are specific to PyDSM and entry points to functions in the delsig module of PyDSM (pydsm.delsig).

Key functions

pydsm.NTFdesign.ntf_schreier()

shorthand for delsig.ntf_schreier()

pydsm.NTFdesign.ntf_chebyshev()

shorthand for delsig.ntf_chebyshev()

pydsm.NTFdesign.ntf_clans()

shorthand for delsig.ntf_clans()

pydsm.NTFdesign.ntf_fir_weighting()

shorthand for weighting.ntf_fir_weighting()

pydsm.NTFdesign.ntf_hybrid_weighting()

shorthand for weighting.ntf_hybrid_weighting()

pydsm.NTFdesign.ntf_fir_minmax()

shorthand for minmax.ntf_fir_minmax()

pydsm.NTFdesign.ntf_dunn()

shorthand for psychoacoustic.ntf_dunn()

pydsm.NTFdesign.ntf_fir_audio_weighting()

shorthand for psychoacoustic.ntf_fir_audio_weighting()

pydsm.NTFdesign.mult_weightings()

shorthand for weighting.mult_weightings()

pydsm.NTFdesign.quantization_noise_gain()

shorthand for merit_factors.quantization_noise_gain()

pydsm.NTFdesign.ntf_fir_minmax()

shorthand for minmax.ntf_fir_minmax()

Submodules

pydsm.NTFdesign.delsig
NTF synthesis functions equivalent to those in pydsm.delsig.
pydsm.NTFdesign.weighting
NTF synthesis techniques that take as their input either a weighting function (indicating the cost of quantization noise power versus frequency) or a specification of the filter in charge of removing the quantization noise.
pydsm.NTFdesign.minmax
NTF synthesis techniques based on a minmax approach.
pydsm.NTFdesign.psychoacoustic
NTF synthesis techniques for audio modulators that result in a noise shaping that take into account psychoacoustics.
pydsm.NTFdesign.merit_factors
Functions for determining merit factors about NTFs.
pydsm.NTFdesign.helpers
Helper functions

Legacy submodule

pydsm.NTFdesign.legacy
Functions that are now superseded but that are worth keeping around for reproducing published results

Deprecated submodules

pydsm.NTFdesign.filter_based
Alternate entry points for some functions.