TCAS-II 2013 paper by Callegari and Bizzarri

This section illustrates how to replicate the results presented in the paper [Cal13b]

[Cal13b]

Sergio Callegari, Federico Bizzarri “Noise Weighting in the Design of ΔΣ Modulators (with a Psychoacoustic Coder as an Example),” IEEE Transactions on Circuits and Systems - Part II: Express Briefs, Vol. 60, N. 11, pp. 756-760. Nov. 2013. DOI: 10.1109/TCSII.2013.2281892. Pre-print available on arXiv.

To this aim, some sample code is provided in the directory Examples/TCAS2-2013.

The research paper provides 4 examples, in Sections IV-a to V-e.

Example in section IV-a

This example refers to the design of a Digital ΔΣ modulator for low pass signals with a mere specification of the signal bandwith (which is specified as a fraction of the modulator sample frequency via the oversampling ratio).

The corresponding code is provided in the file demo_lp_brickwall.py. Once PyDSM and all its pre-requisites are installed, this can be started directly by opening a shell (command prompt) and typing:

python demo_lp_brickwall.py

Alternatively, the script can be opened in Spyder and launched from there.

The code runs, showing some intermediate output from the optimizer. Then it provides the graphical output that is delivered in Figure 2 in the TCAS-II paper.

The proposed example code is not particularly elegant, but should be rather easy to read, also thanks to the many comments.

Example in section IV-b

This example refers to the design of a multiband Digital ΔΣ modulator.

The corresponding code is provided in the file demo_multiband_brickwall.py. Once PyDSM and all its pre-requisites are installed, this can be started directly by opening a shell (command prompt) and typing:

python demo_multiband_brickwall.py

Alternatively, the script can be opened in Spyder and launched from there.

The code runs, showing some intermediate output from the optimizer. Then it provides the graphical output that is delivered in Figure 3 in the TCAS-II paper.

The proposed example code is not particularly elegant, but should be rather easy to read, also thanks to the many comments.

Example in section IV-c

This example refers to the design of a Digital ΔΣ modulator that is followed by a non ideal filter in charge of removing the quantization noise.

The corresponding code is provided in the file demo_lp_filter.py. Once PyDSM and all its pre-requisites are installed, this can be started directly by opening a shell (command prompt) and typing:

python demo_lp_filter.py

Alternatively, the script can be opened in Spyder and launched from there.

The code runs, showing some intermediate output from the optimizer. Then it provides the graphical output that is delivered in Figure 5 in the TCAS-II paper.

The proposed example code is not particularly elegant, but should be rather easy to read, also thanks to the many comments.

Example in section IV-d

This example refers to the design of a Digital ΔΣ modulator for low-pass signal that is capable of delivering a reduced quantization noise close to dc.

The corresponding code is provided in the file demo_lp_low_dc_noise.py. Once PyDSM and all its pre-requisites are installed, this can be started directly by opening a shell (command prompt) and typing:

python demo_lp_low_dc_noise.py

Alternatively, the script can be opened in Spyder and launched from there.

The code runs, showing some intermediate output from the optimizer. Then it provides the graphical output that is delivered in Figure 6 in the TCAS-II paper.

The proposed example code is not particularly elegant, but should be rather easy to read, also thanks to the many comments.

Example in section IV-e

This example refers to the design of a Digital ΔΣ modulator for audio signals capable of shaping the in-band quantization noise according to a psychoacoustic weighting function, so that it is minimally audible.

The corresponding code is provided in the file demo_psychoacoustic.py. Once PyDSM and all its pre-requisites are installed, this can be started directly by opening a shell (command prompt) and typing:

python demo_psychoacoustic.py

Alternatively, the script can be opened in Spyder and launched from there.

The code runs, showing some intermediate output from the optimizer. Then it provides the graphical output that is delivered in Figure 7 in the TCAS-II paper.

The proposed example code is not particularly elegant, but should be rather easy to read, also thanks to the many comments.