This blog details a method to allow somebody who is visually impaired to easily listen to their CD collection again. My Mother lost her eyesight through macular degeneration. She has a decent collection of classical music that she built up over a few years. But she can’t see well enough to easily use a CD player anymore. On top of that, her mobility is restricted. I bought her a Roberts Concerto 2 CD player and radio designed for the visually impaired. Please find details and a review here. This is the best that I could find, but it is still fiddly and difficult for somebody without sight to load the CD. It is quite a bulky device, which makes putting it next to an elderly person awkward as it takes up most of chair side table, or the user has to get up and go to where it is placed. Which is a barrier to it ever being used when just getting out of a chair is no longer straight forwards. I looked at a few potential solutions. I found a portable CD player on eBay and tried that. But again, it takes up a little too much table top space and it is fiddly to load. You don’t realise how poorly controls are laid out on most devices until you try to explain to somebody without vision how to use it. I found some lovely projects where custom built players are built using tags. Audio books are loaded onto a memory card and played using something like a Raspberry Pi single board computer. An NFC coil is used to read a tag placed inside the case of an audiobook or CD and the audio is played from the memory card. Here is an example on Hackaday. I started going down this route. Then had another think. This will add another device to my mother’s chair side table. I will have to:
Run off all her CDs to memory card.
Show her how to use it.
My mother uses a Sovereign USB stick player to listen to her talking books and newspapers. This is a well designed player aimed at the visually impaired. It has decent sound quality. The build cost of a custom device would exceed the cost of a Sovereign and for me to think I would match the sound quality is a tad arrogant. One of the design features of this player is that it will remember the place you were last at on the stick. You can even swap sticks and it remembers the last play position on the previous five sticks you played. Mum already has this next to her and knows how to use it. As a side note, there is now a smaller version of this player available called the sonic which I bought to listen to my podcasts with and loan to Mum when she visits.
So I ran off her CDs to MP3 and put each one on to a cheap USB stick. I bought some small key rings and used these to connect the memory sticks to postcards on which I printed the title of the CD. So far I have run off 10 of these. These 10 sticks and labels fit in an little box on her table next to the player. If the idea works, I will run off some more.
I’ve written this up so that other people in my position have a potential solution to enable others with disability to enjoy their music collections.
Having to pay a couple of dollars each for a cheap USB stick from eBay for each CD may seem a tad pricey, but compared with the time and cost of building a custom device, I think it is money well invested.
The proof of the pudding is in the eating. Please find a photo of Mum at my house, having dozed off while listening to Aled Jones with the bear she gave me about 40 years ago. The bear’s arms are a little saggy now, but we all get a little infirm and need some help as we age.
This article gives an example of how to use an exponentially weighted moving average filter to remove noise from a data set using the pandas library in python 3. I am writing this as the syntax for the library function has changed. The syntax I had been using is shown in Connor Johnoson’s well explained example here.
I will give some example code, plot the data sets then explain the code. The pandas documentation for this function is here. Like a lot of pandas documentation it is thorough, but could do with some more worked examples. I hope this article will plug some of that gap.
Here’s the example code:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
ewma = pd.Series.ewm
x = np.linspace(0, 2 * np.pi, 100)
y = 2 * np.sin(x) + 0.1 * np.random.normal(x)
df = pd.Series(y)
# take EWMA in both directions then average them
fwd = ewma(df,span=10).mean() # take EWMA in fwd direction
bwd = ewma(df[::-1],span=10).mean() # take EWMA in bwd direction
filtered = np.vstack(( fwd, bwd[::-1] )) # lump fwd and bwd together
filtered = np.mean(filtered, axis=0 ) # average
plt.title('filtered and raw data')
plt.plot(y, color = 'orange')
This produces the following plot. Orange line = noisy data set. Blue line = backwards filtered EWMA data set. Red line = forwards filtered EWMA data set. Green line = sum and average of the two EWMA data sets. This is the final filtered output.
Let’s look at the example code. After importing the libraries I will need in lines 1-5, I create some example data. Line 6 creates 100 x values with values spaced evenly from 0 to 2 * pi. Line 7 creates 100 y-values from these 100 x-values. Each y value = 2*sin(x)+some noise. The noise is generated using the np.random.normal function. This noisy sine function is plotted in line 15 and can be seen as the jagged orange line on the plot.
Forwards and backwards EWMA filtered data sets are created in lines 10 and 11.
Line 10 starts with the first x-sample and the corresponding y-sample and works forwards and creates an EWMA filtered data set called fwd. This is plotted in line 17 as the red line.
Line 11 starts at the opposite end of the data set and works backwards to the first – this is the backwards EWMA filtered set, called bwd. This is plotted in line 18 as the blue line.
These two EWMA filtered data sets are added and averaged in lines 12-13. This data set is called filtered. This data set is plotted in line 16 as the green line.
If you look at the ewma functions in line 10 and 11, there is a parameter called span. This controls the width of the filter. The lag of the backwards EWMA data behind the final averaged filtered output is equal to this value. Similarly the forward EWMA data set has an offset forwards of the noisy data set equal to this value. Increasing the span increases the smoothing and the lag. Increasing the value will also reduce the peaks of the filtered data in relation to the unfiltered data. You need to try out different values.
My present application for this filter is removing jitter from accelerometer data. I have also used this filter to smooth signals from hydrophones.