Concept

Web design

Related notebooks (7)

Fairness Week 1: University Admissions – Solutions

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

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

Filtering Music: Extracting Audio Frequencies Using Filters

Filtering Music[1]¶

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

In this notebook we will design and use filters to extract specific audio frequencies from a music file. In [1]: %matplotlib inline import matplotlib.pyplot as plt import numpy as np import IPython import scipy.signal as sp from scipy.io import wavfile infringment...) In [2]: IPython.display.Audio(s, rate=SF) Out[2]: Your browser does not support the audio element.

Extracting the bas

Adaptive Echo Cancellation using LMS Filter

Adaptive Echo Cancellation[1]¶

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

In this notebook we will use the adaptive LMS filter to estimate the impulse response of a reverberating room. A typical use case is handsfree telephony: since the received signal, amplified and played by the speaker, is picked up by the microphone, we need to eliminate it from the signal that is transmitted back in order to avoid a feedback loop. The signal picked up by the microph

Simulating a Telephone Channel using FIR Filter Design

A Telephone Channel Simulator[1]¶ In this notebook we will develop a filter that simulates the effect of a standard telephone channel. In [1]: %matplotlib inline import matplotlib.pyplot as plt import numpy as np import IPython import scipy.signal as sp from scipy.io import wavfile In [2]: plt.rcParams["figure.figsize"] = (14,4) implicit sampling rate will be the internal "clock" of our simulation. In [3]: IPython.display.Audio(s, rate=SF) Out[3]: Your browser does not support

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

Designing FIR Filters Using the Parks-McClellan Algorithm

The Parks-McClellan Filter Design Method[1]¶

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

The Parks-McClellan (PM) algorithm is a clever application of advanced polynomial fitting techniques to the problem of FIR filter design. In this notebook we will explore the key ideas behind the method by considering the design of a simple Type-I lowpass filter. NOTE: This notebook uses interactive widgets. If the interactive controls do not work or show up, please fo

Advantages of Digital Transmission over Analog Transmission

[1]Paolo Prandoni, [2]EPFL Analog vs Digital Transmission In this notebook we will explore the advantages of digital transmission over analog transmission. We will model the case of transmission over a long (e.g. transoceanic) cable in which several repeaters are used to compensate for the attenuation incurred by the signal. In [1]: import matplotlib import matplotlib.pyplot as plt import numpy as np import IPython from scipy.io import wavfile plt.rcParams["figure.figsize"] = (14,4)

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.