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In digital signal processing, upsampling, expansion, and interpolation are terms associated with the process of resampling in a multi-rate digital signal processing system. Upsampling can be synonymous with expansion, or it can describe an entire process of expansion and filtering (interpolation). When upsampling is performed on a sequence of samples of a signal or other continuous function, it produces an approximation of the sequence that would have been obtained by sampling the signal at a higher rate (or density, as in the case of a photograph). For example, if compact disc audio at 44,100 samples/second is upsampled by a factor of 5/4, the resulting sample-rate is 55,125. Rate increase by an integer factor L can be explained as a 2-step process, with an equivalent implementation that is more efficient: Expansion: Create a sequence, comprising the original samples, separated by L − 1 zeros. A notation for this operation is: Interpolation: Smooth out the discontinuities with a lowpass filter, which replaces the zeros. In this application, the filter is called an interpolation filter, and its design is discussed below. When the interpolation filter is an FIR type, its efficiency can be improved, because the zeros contribute nothing to its dot product calculations. It is an easy matter to omit them from both the data stream and the calculations. The calculation performed by a multirate interpolating FIR filter for each output sample is a dot product: where the h[•] sequence is the impulse response of the interpolation filter, and K is the largest value of k for which h[j + kL] is non-zero. In the case L = 2, h[•] can be designed as a half-band filter, where almost half of the coefficients are zero and need not be included in the dot products. Impulse response coefficients taken at intervals of L form a subsequence, and there are L such subsequences (called phases) multiplexed together. Each of L phases of the impulse response is filtering the same sequential values of the x[•] data stream and producing one of L sequential output values.
[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 [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 [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 [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.