Concept

Optical filter

Related notebooks (10)

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

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

Digital Simulation of Guitar Feedback in I Feel Fine by The Beatles

I Feel Fine, Digitally[1]¶ This notebook is a little musing inspired by the "internal mechanics" of a very famous audio snippet, namely the guitar sound at the beginning of the song "I Feel Fine", written and recorded by the Beatles in 1964. The historical significance of the record lies in the following claim by John Lennon:

 "I defy anybody to find a record... unless it is some old blues
 record from 1922... that uses feedback that way. So I claim it for
 the Beatles. Before H

Implementing a Fractional Resampler using Local Lagrange Interpolation

FIR Resampling[1]¶

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

In this notebook we will implement a simple fractional resampler that uses local Lagrange interpolation. The resampler can be used to perform any rational sampling rate change (but beware of aliasing when downsampling!) In [1]: import numpy as np import IPython from scipy.io import wavfile Local Lagrange interpolation[4]¶ To perform resampling we will need to compute subsample values and, in

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

Modeling Epidemics with Signal Processing Techniques

Epidemics and Signal Processing[1]¶

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

In [1]: import matplotlib.pyplot as plt import numpy as np import scipy.signal as sp import seaborn as sns sns.set() In [2]: plt.rcParams["figure.figsize"] = (14,4) The basic reproduction number[4]¶ The propagation mechanism of an epidemic such as the one caused by SARS-CoV-2 virus can be roughly modeled, at least in its inital phase, as a process in which each infected individ

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

Voice Transformation Techniques Using Digital Signal Processing

A Gallery of Voice Transformers[1]¶

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

In this notebook we will explore various ways to implement a system that changes the voice of a speaker. Typical applications for these techniques, strictly sorted by plausibility, include international spy missions and prank telephone calls, and devices such as the one displayed here can be easily bought on the web... or in a novelty shop. No description has been provided for t

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

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