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Related notebooks (16)

Block Processing of Audio Files Using Stateful FIR Filters

Block-processing of audio files[1]¶

                                      [2]Paolo Prandoni, [3]LCAV, EPFL

This notebook shows a simple way to process audio files (in WAV format) in a block-by-block fashion; this is useful in two cases 1. when you want to process a data stream in real time but you can accept a small processing delay; in this case a buffered approach can yield computational gains 2. when you need to process a large file that does not fit entirely into

Drude Model

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Drude Model¶{.anchor-link} {#Drude-Model}

Plots the dielectric function and loss function in the Drude model ::: ::: ::: ::: ::: {.jp

Calculation and plotting of some important quantities relative to the electron s properties in a conventional TEM

Fairness Week 2: COMPAS and Fairness in Machine Learning – Solutions

Fairness Week 2: Predicting recidivism¶{.anchor-link} {#Fairness-Week-2:-Predicting-recidivism}

How to use this notebook Simply read the text and follow the instructions.
This notebook contains code cells, which can be modified and must be executed to see the result of their content.
To execute a cell, select it and click on the play button (▶) in the tool bar, or type Shift + Enter or Ctr + Enter. As the variables contained in a cell are stor

Safety Week 1: Content Moderation – Solutions

::: {style="border-left: 6px solid rgba(69, 157, 185, 1); border-radius:5px; box-shadow: 3px 3px 3px rgba(221, 221, 221, 1);"} How to use this notebook ::: {style="padding: 0 0 2px 10px;"} Simply read the text and follow the instructions.
This notebook contains code cells, which can be modified and must be executed to see the result of their content.
To execute a cell, select it and click on the play button (▶) in the tool bar, or type Shift + Enter or Ctr + Enter. As the variables conta

Introduction: Python and ethical dilemmas

Introduction: Python and ethical dilemmas¶{.anchor-link} {#Introduction:-Python-and-ethical-dilemmas}

Notebook by Athina Papageorgiou Koufidou, Cécile Hardebolle and the Responsible software team (2025). Except where otherwise noted, the content of this notebook is licensed under a {rel="license"}Creative Commons Attribution International License (CC BY 4.0 International).
[](http://creativecommons.org/licenses

Sustainability Week 2: Embodied Impacts and Hardware LCA – Solutions

Sustainability Week 2: Embodied impacts¶{.anchor-link} {#Sustainability-Week-2:-Embodied-impacts}

Sustainability Week 1: The Carbon Footprint of Algorithms – Solutions

{#Sustainability-Week-1:-the-Carbon-Footprint-of-Algorithms} How to use this notebook Simply read the text and follow the instructions.
This notebook contains code cells, which can be modified and must be executed to see the result of their content.
To execute a cell, select it and click on the play button (▶) in the tool bar, or type Shift + Enter or Ctr + Enter. As the variables contained in a cell are stored in memory, the order of execution of the cells is important! Notebook by M

Fairness Week 1: University Admissions – Solutions

Fairness Week 1: University admissions¶{.anchor-link} {#Fairness-Week-1:-University-admissions}

Interactive Phonon Band Structure Visualization

In [1]: import numpy as np import matplotlib.pyplot as plt from matplotlib import animation from matplotlib import rc import ipywidgets as ipw from ipywidgets import interact,interactive import ipywidgets as widgets from IPython.display import HTML %matplotlib widget global global_K1 global global_K2 global global_branche global global_selected_k In [2]: def get_closest_point(clic_x,clic_y): # Get the data from the figure ax= plt.gca() # get axis handle acoustic_data = ax.lines[1] op

Modeling and Analysis of a PN-Junction in Inhomogeneous Semiconductors.

In [1]: import matplotlib.pyplot as plt import matplotlib.ticker as mtick import numpy as np import ipywidgets as widgets import scipy.integrate as integrate from ipywidgets import interact, fixed from scipy.interpolate import interp1d from textwrap import dedent from bokeh.io import push_notebook, show, output_notebook, output_file, curdoc from bokeh.plotting import figure from bokeh.layouts import column, row from bokeh.themes import built_in_themes from bokeh.models import Range1d, BasicTickFormatter, L

Investigating the Effect of Phase on Sound Perception

Hearing the phase of a sound[1]¶

                                      [2]Paolo Prandoni, [3]LCAV, EPFL

In this notebook we will investigate the effect of phase on the perceptual quality of a sound. It is often said that the human ear is commercial-grade audio equipment takes place in the magnitude domain only. In [1]: %matplotlib inline import matplotlib import matplotlib.pyplot as plt import numpy as np import IPython from scipy.io import wavfile In [2]: plt.rcParams["figure.fig

Empowerment Week 2: Explainable AI and Model Interpretability – Solutions

Empowerment Week 2 Exercise: Explainability and Transparency¶{.anchor-link} {#Empowerment-Week-2-Exercise:-Explainability-and-Transparency}

Exploring Quantization and Oversampling through One-Bit Music

One-Bit Music[1]¶ In this notebook we will talk about quantization and oversampling and we will do so by taking a trip down memory lane to revisit the early from monophonic square waves, introduce polyphony by way of pulse-width modulation and finish with the basics of sigma-delta quantization. pacman In [1]: import matplotlib import matplotlib.pyplot as plt import numpy as np import scipy.signal as sp from scipy.io import wavfile import IPython In [2]: import sys ! wget -q -O mus

Understanding and Plotting the Discrete Fourier Transform (DFT)

Plotting the DFT[1]¶

                                      [2]Paolo Prandoni, [3]LCAV, EPFL

In this notebook we will look at the practical issues associated to plotting the DFT and in particular the DFT of real-world signals. We will examine how to map the DFT coefficients to real-world frequencies and we will investigate the frequency resolution of the DFT and the effects of zero padding. In [1]:

first our usual bookkeeping

%matplotlib inline import matplotlib import matplotlib.p

Whistle-Activated Keylock System Implementation

Whistling Keylock[1]¶ In this notebook we will implement a simple "whistle-activated" keylock, that is, an access control system where the password is a pre-defined melody. To program the lock, you record your musical passphrase and then, to open the lock, you whistle it back. Although the implementation will be simple and skip most of the fine details that would be necessary to produce a truly robust system, we need to take into account at least the following points: * The syste

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