The Multimedia Lab is designed to discuss two aspects of the life-cycle of a multimedia file. This story begins with generation of the file, which is the digitization of some analog phenomenon of interest (e.g. sound -> music, light -> image, both -> film). Once this process is complete, the generated digital media file can be operated on (stored, read, copied, etc.) by a finite precision computer. Each of these operations, however, would be quite a bit easier if the file were small, so we'll investigate compressing the file into a more compact representation.

Naturally, the process of capturing and storing a media file is composed of a harmony of instruments across **all** EECS disciplines, but this lab will focus in particular on the role of probabilistic analysis, reasoning, and algorithm design in this symphony. Probability in EECS does not exist in a vacuum but, as you will see in this lab, often forms the theoretical underpinning to analyze the efficacy of an idea.

When capturing media, we must go from a real world analog media to a digital representation. In general, this process is a matter of sampling, quantization, and compression. As technology improves, sampling methods become faster and more precise, quantized representations of media allow for more bits of precision, and both lossy and lossless compression algorithms improve performance. The tools to measure the effectiveness of these improving technologies, however, remain largely the same, and many of them are built on probabilistic analysis.

In this lab, we will talk about quantization for audio signals, and compression of tweets. This should give you an introduction to two of the key aspects of digitally storing media.

We live in an analog world, where input values may be continous and uncountable. This isn't very useful if we want to do any sort of digital signal processing. What we do instead to deal with this is to create a mapping from our huge set of continuous input values to a smaller set that we can manage. We essentially bucket-off all of our input values before working with them (rounding is a form of quantization as well). However, as you might imagine, by mapping a large set to a smaller one, we introduce some level of quantization error. The amount of distortion you experience will be determined by the method and parameters used during the quantization process.

In this portion of the lab, we will step you through the process of generating and playing a wav file in iPython. You will then implement a quantizer method, and play the resulting quantized song. We will wrap up this section of the lab by exploring the effects of quantization error.

The following modules and functions will allow you to generate, load, and play a wav file in iPython. You don't need to understand how they work, but you should understand how to use them.

In [ ]:

```
import scipy.constants as const
import scipy
from scipy.io import wavfile
from IPython.core.display import HTML
from IPython.display import display
import numpy as np
import matplotlib.pyplot as plt
from __future__ import division
%matplotlib inline
# this is a wrapper that take a filename and publish an html <audio> tag to listen to it
def wavPlayer(filepath):
src = """
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title>Simple Test</title>
</head>
<body>
<audio controls="controls" style="width:600px" >
<source src="files/%s" type="audio/wav" />
Your browser does not support the audio element.
</audio>
</body>
"""%(filepath)
display(HTML(src))
def playMusic(title, rate, music):
# write the file on disk, and show in in a Html 5 audio player
music_wav = music * 2**13
wavfile.write(title + '.wav', rate, music_wav.astype(np.int16))
wavPlayer(title + '.wav')
```

Each key of a piano can be thought as a sinusoidal wave with a certain frequency. Let us consider the following score of a song, 'School Bell Ringing'.

Let us assume that each note is a half second long sinusoidal wave. Because the song consists of 32 notes, the song is 16 seconds long. If we specify a sampling rate of 44.1 kHz, then our song will have $44100\times16$ samples in the entire song. The frequency associated with each note will be specified by the formula given for piano key frequencies, namely that the frequency for the $n^\text{th}$ key is given by

$$ {\huge f(n) = 2^{\frac{n-49}{12}}\times 440 \text{Hz} }$$The list $\operatorname{notes}$ specified below holds the key values $n$ for each note in 'School Bell Ringing' in sequential order.

In [ ]:

```
# C:40, D:42, E:44, F:45, G:47, A:49, B:51, Higher C:52
# 0 represents 'pause'
notes = [47, 47, 49, 49, 47, 47, 44, 44, 47, 47, 44, 44, 42, 42, 42, 0,
47, 47, 49, 49, 47, 47, 44, 44, 47, 44, 42, 44, 40, 40, 40, 0]
```

From these $n$ values, we can generate the sinusoids which represent this song as follows:

In [ ]:

```
note_duration = 0.5 # per key, in sec
rate = 44100 #44.1 khz
music_duration = note_duration * len(notes)
time = np.linspace(0, music_duration, num=rate*music_duration) # indices for 0-16 secs spaced apart by 1/44100
song = np.zeros(len(time))
sinwave = lambda f,t : np.sin(2*np.pi*f*t) # generate a sinusoid of frequency f Hz over time t
key2freq = lambda n : 2 ** ((n-49)/12) * 440 # convert a piano key n to a frequency in Hz
idx_note = 0
for note in notes:
if note: # if note == 0, skip.
freq = key2freq(note) # frequency of each note
song[idx_note*rate*note_duration : (idx_note+1)*rate*note_duration-1] = \
sinwave( freq,
time[idx_note*rate*note_duration : (idx_note+1)*rate*note_duration-1 ]
) #generates the sinusoids for the song, in .5 sec intervals
idx_note += 1
```

Now that we've generated the song, let's plot the first 0.01 seconds to see that we actually turned notes into sinusoidal waves.

In [ ]:

```
plt.plot(time[0:441], song[0:441]) # Plot the first 0.01 second
plt.title("First 0.01 seconds of 'School Bell Ringing'")
plt.xlabel("Time")
plt.ylabel("Amplitude")
```

And now, let's listen to the song that's been generated.

In [ ]:

```
playMusic('school_bell_original', rate, song)
print "Wow! What a great song. I should try playing other ones too."
```

Now for the fun part. We want to quantize this audio signal and analyze the resulting error. We will use an $\ell$-bit uniform-quantizer. We can use only $L = 2^\ell$ quantized values: $\{-1, -\frac{L-3}{L-1}, -\frac{L-5}{L-1}, ..., -\frac{1}{L-1}, \frac{1}{L-1}, ..., \frac{L-5}{L-1}, \frac{L-3}{L-1}, 1\}$ to represent each value of the original signal $-1 \leq x[n] \leq 1$. Our signal only takes values between -1 and 1, so our $\ell$-bit uniform-quantizer allows our signal to take on $2^\ell$ evenly spaced values in the range of interest. Each point in the original signal $x[n]$ is binned according to a nearest neighbor strategy.

In [ ]:

```
def quantizer(num_of_bits, original_signal):
# Complete this quantizer method.
# It should return a numpy array of the same length that contains quantized values of the original signal.
# your beautiful code here... #
return quantized_signal
```

1b. Your Answer Here

In [ ]:

```
# Solution for 4-bit quantizer should match below
num_of_bits = 4
quantized_song = quantizer(num_of_bits,song)
# Plot the first 0.01 second of songs.
plt.figure(figsize=(10,6))
plt.plot(time[0:441], song[0:441])
plt.plot(time[0:441], quantized_song[0:441])
plt.legend(['Original Signal', 'Quantized Signal'])
plt.title('Effects of ' + str(num_of_bits) + '-bit quantizer')
playMusic('quantized_song_' + str(num_of_bits) + '_bits', rate, quantized_song)
```

As noted before, the difference you are witnessing between the original signal and the quantized signal is the **quantization
error**. As confirmed from the previous exercise, the quantization noise is almost negligible if one uses a sufficient number of bits. Let's take a look at the quantization error more carefully.

In [ ]:

```
# Run code in this cell to see the difference between the original and quantized signals (quantization error)
num_of_bits = 1
quantized_song = quantizer(num_of_bits,song)
plt.plot(time[0:441], song[0:441] - quantized_song[0:441])
plt.title('Quantization Error')
```

Note: The MSE is the $l^2$-norm squared of the quantization noise divided by the number of noise samples.

In [ ]:

```
# Q2a. Your Beautiful Code Here
```

You should see that the MSE does indeed drop exponentially! This result was easily confirmed via simulation, so let's do some calculations to obtain the same results via analysis.

In order to analyze the effect of quantization noise, we need to come up with a good model for the quantization noise. Although everything we did was deterministic, what we saw above was really just one specific realization of quantization. For each sample, the original value was mapped to the nearest quantization point. The distance between the chosen quantization point and the original value became the quantization noise for the sample. But how bad is the error on the whole?

Let's just start by modelling the noise as a **uniform random variable**. If the original signal is assumed to take each value chosen at uniformly random, the model will be precise. However, is this model also valid for structured signals such as a combination of sinusoidal waves? Let's take a look at a 'noise histogram' to find out.

In [ ]:

```
num_of_bits = 8
# time, song = sampler(sampling_rate)
quantized_song = quantizer(num_of_bits, song)
plt.hist( (song - quantized_song ) , 30 );
plt.title('Noise Histogram')
plt.xlabel('Quantization Error')
plt.ylabel('Occurances')
```

We see that the quantization error is not exactly uniform, but extremeley close. There is one value which occurs significantly more frequently than the rest, but that is an artifact of the structure in a sinusoid (they like to hang around -1 and 1 for a while). If we consider general signals, the model of quantization noise as uniform is even more accurate, although it is not too bad in this case either.

Q2b. Your Solution Here

In [ ]:

```
# Q2b. Plotting Code Here
```

Before attempting this section, brush up on (or learn for the first time) Huffman coding.

Your friend Ben Bitdiddle just returned from Tunisia and wants to start a revolution at UC Berkeley. He knows Twitter can be an important tool for revolution, but it has a major problem: 140 characters just isn't enough. For example, Ben wants to send the tweet:

The tweet exceeded 140 characters and he had to send it at **2 seperate tweets**, which dramatically reduces the impact of his revolutionary message. If people read the first tweet, they won't know where the protest is located; if they read the second, they may get to the protest, but they won't know why they are protesting!

Ben has devised a new scheme to *compress* his tweets so he can fit his longer messages into 140 characters. There are only 32 characters which he uses in his tweets (26 letters + 6 punctuation), but not all of them occur equally often, so he will use a Huffman code as follows:

- He will encode his tweet as a binary string using a Huffman code based on the letter frequencies determined by the hash table
`freq_dict`

below. - He will convert this bit-string back into letters according to the
`bits2letters`

mapping defined below. If the bitstring created by the Huffman code does not have a length which is a multiple of 5, he will append the Huffman code for the character`SPACE`

to the bitstring until it*does*have length which is a multiple of 5.

In [ ]:

```
english_freq_dict = {'!': 0.0008, "'": 0.0004, ' ': 0.1033, ',': 0.0011,\
'.': 0.0011, '?': 0.0006, 'a': 0.069, 'c': 0.027000000000000003,\
'b': 0.013999999999999999, 'e': 0.1, 'd': 0.039, 'g': 0.021, 'f': 0.022000000000000002,\
'i': 0.06, 'h': 0.048, 'k': 0.008, 'j': 0.0014000000000000002, 'm': 0.025,\
'l': 0.043, 'o': 0.065, 'n': 0.06, 'q': 0.0017000000000000001, 'p': 0.02,\
's': 0.055999999999999994, 'r': 0.052000000000000005, 'u': 0.027000000000000003,\
't': 0.083, 'w': 0.017, 'v': 0.0108, 'y': 0.018000000000000002, 'x': 0.0036, 'z': 0.0012}
```

In [ ]:

```
letters2bits = {'!': '11101', "'": '11111', ' ': '11010', ',': '11100', \
'.': '11011', '?': '11110', 'a': '00000', 'c': '00010', 'b': '00001', 'e': '00100', \
'd': '00011', 'g': '00110', 'f': '00101', 'i': '01000', 'h': '00111', 'k': '01010',\
'j': '01001', 'm': '01100', 'l': '01011', 'o': '01110', 'n': '01101', 'q': '10000', \
'p': '01111', 's': '10010', 'r': '10001', 'u': '10100', 't': '10011', 'w': '10110', 'v': '10101',\
'y': '11000', 'x': '10111', 'z': '11001'}
def reverse_dict(original_dict):
# create a new dict with the keys of original_dict as values and the values of original_dict as keys
new_dict = {}
for key in original_dict:
new_dict[original_dict[key]] = key
return new_dict
bits2letters = reverse_dict(letters2bits)
```

Characters and their frequencies in the English language:

Character | Frequency |
---|---|

SPACE | 0.1033 |

e | 0.1 |

t | 0.083 |

a | 0.069 |

o | 0.065 |

i | 0.06 |

n | 0.06 |

s | 0.056 |

r | 0.052 |

h | 0.048 |

l | 0.043 |

d | 0.039 |

c | 0.027 |

u | 0.027 |

m | 0.025 |

f | 0.022 |

g | 0.021 |

p | 0.02 |

y | 0.018 |

w | 0.017 |

b | 0.014 |

v | 0.0108 |

k | 0.008 |

x | 0.0036 |

q | 0.0017 |

j | 0.0014 |

z | 0.0012 |

, | 0.0011 |

. | 0.0011 |

! | 0.0008 |

? | 0.0006 |

' | 0.0004 |

In [ ]:

```
from heapq import heappush, heappop, heapify
from collections import defaultdict
def HuffEncode(freq_dict):
"""Return a dictionary (letters2huff) which maps keys from the input dictionary freq_dict
to bitstrings using a Huffman code based on the frequencies of each key"""
# Your Beautiful Code Here #
return letters2huff
```

In [ ]:

```
letters2huff = HuffEncode(english_freq_dict)
huff2letters = reverse_dict(letters2huff)
# print dictionary in readable format
```

In [ ]:

```
def encode_string(string, letters2huff):
"""Return a bitstring encoded according to the Huffman code defined in the dictionary letters2huff.
If your resulting bitstring does not have a length which is a multiple of five, add the binary for
SPACE characters until it is."""
# Your Beautiful Code Here
return encoded_string
```

In [ ]:

```
original_string = 'grades are a fundamentally oppressive tool used by academic institutions to equate the value of a human being with a letter and a number! protest coryhall tonight'
encoded_string = encode_string(original_string, letters2huff)
print "Original String: " , original_string
print "Length of Original String: " , len(original_string)
print "Encoded String: " , encoded_string
print "Length of Encoded String: " , len(encoded_string)
```

>>> `decode_string(encode_string('hello world', letters2huff), huff2letters)`

`"hello world"`

In [ ]:

```
def decode_string(coded_string, letters2huff):
"""Translate from a Huffman coded string to a regular string"""
# Your Code Here #
return decoded_string
```

In [ ]:

```
print decode_string(encode_string('hello world', letters2huff), huff2letters)
print decode_string(encode_string(original_string, letters2huff), huff2letters)
```

After implementing the coding system described above, Ben was able to tweet the following (138 characters), while still conveying his full, original message:

Wow! That's the kind of tweet that will really get me excited to revolt!

Q3e. Your Solution Here

Congratulations, you can now quantize children's songs and Huffman code tweets. You're really moving up in the world!

Now that we've looked at the Huffman compression algorithm as applied to a tweet, let's see if you can apply a Huffman code to a song. In fact, the last step in encoding audio into an mp3 file is to apply a Huffman code, so this is not an impractical application. Walk through the following code blocks (everything should work properly if you implemented all of the above functions correctly). The code will compress the song `School Bell Ringing`

. Each code block has a comment describing its functionality at the top.

Generate a quantized version of `School Bell Ringing`

:

In [ ]:

```
def generate_quantized_song(num_of_bits):
# Code to create song
# C:40, D:42, E:44, F:45, G:47, A:49, B:51, Higher C:52
notes = [47, 47, 49, 49, 47, 47, 44, 44, 47, 47, 44, 44, 42, 42, 42, 0,
47, 47, 49, 49, 47, 47, 44, 44, 47, 44, 42, 44, 40, 40, 40, 0]
note_duration = 0.5 # per key, in sec
fs = 1200
music_duration = note_duration * len(notes)
time = np.linspace(0, music_duration, num=fs*music_duration) # indices for 0-16 secs spaced apart by 1/44100
song = np.zeros(len(time))
sinwave = lambda f,t : np.sin(2*np.pi*f*t) # generate a sinusoid of frequency f Hz over time t
key2freq = lambda n : 2 ** ((n-49)/12) * 440 # convert a piano key n to a frequency in Hz
idx_note = 0
for note in notes:
if note: # if note == 0, skip.
freq = key2freq(note) # frequency of each note
song[idx_note*fs*note_duration : (idx_note+1)*fs*note_duration-1] = \
sinwave( freq,
time[idx_note*fs*note_duration : (idx_note+1)*fs*note_duration-1 ]
)
idx_note += 1
quantized_song = quantizer(num_of_bits, song)
return time, quantized_song
```

In [ ]:

```
num_of_bits = 4 # number of bits used by the quantizer
time, quantized_song = generate_quantized_song(num_of_bits) # generate song quantized with num_of_bits
fs = 3000 # I want the song to playback faster and with a higher pitch, so I'm increasing playback sampling frequency
```

Visualize the first 0.01 seconds of the song and play it:

In [ ]:

```
plt.plot(time[:120], quantized_song[:120]) # Plot the first 0.01 second
plt.title("First 0.01 seconds of 'School Bell Ringing' Quantized")
playMusic('sbr_' + str(num_of_bits) + '_bit_quantized', fs, quantized_song)
```

Plot a histogram of song values (i.e. how often each of the $2^{num\_of\_bits}$ points occur) and use this as a frequency dictionary to encode the song:

In [ ]:

```
# Plot Histogram
plt.hist(quantized_song, 2**num_of_bits)
plt.title("Historgram of Song Values")
# Set frequency dictionary values based on histogram
L = 2**num_of_bits
hist, bins = np.histogram(quantized_song, L)
quantized_pt = -1
freq_dict = {}
for value in hist:
freq_dict[ quantized_pt ] = value
quantized_pt += 2 / (L-1)
```

Encode the song and compare the differences in file size:

In [ ]:

```
def encode_song(song, huff):
encoded = ''
for value in song:
encoded += huff[huff.keys()[np.argmin(abs([huff.keys()] - value))]]
return encoded
def decode_song(song, huff):
dhuff = reverse_dict(huff)
original = np.array([])
current = ''
for i in song:
current += i
if current in dhuff:
original = np.r_[original, dhuff[current]]
current = ''
return original
```

In [ ]:

```
huff = HuffEncode(freq_dict)
print "Symbol\tWeight\tHuffman Code"
total_num_of_bits = 0
for p in huff:
print "%.3f\t%s\t%s" % (p, freq_dict[p], huff[p])
total_num_of_bits += freq_dict[p] * len(huff[p])
encoded = encode_song(quantized_song, huff)
print 'First 50 bits of huffman coded song file: ', encoded[:50]
original_num_of_bits = num_of_bits * quantized_song.size
original_size = round(original_num_of_bits/1000/8) # original file size
print "Original file size with a %d-bit quantizer: %d KB" % (num_of_bits, original_size)
print "Compressed file size with a %d-bit quantizer: %d KB" % (num_of_bits, round(total_num_of_bits/1000/8))
print "Wow! Look at that compression!"
```

Shannon and Fano used a simple rounding method on the entropy function to generate their compression algorithm, but they could not prove that this was the optimal compression achievable with an integer length code (because it was not, in fact, optimal). At the time, Huffman was a student in one of Fano's courses at MIT, where Fano gave students the option of taking a final or solving the problem of finding an optimal binary representation of an I.I.D. sequence of elements from some distribution. Huffman decided to try his hand at this problem, eventually leading to his discovery of the Huffman compression algorithm. He was also able to prove that this scheme was optimal.

Huffman has stated on many occasions that if he were aware that both Shannon and Fano had struggled with this problem, he probably wouldn't even have tried and simply studied for the final exam. Absent this information, however, he was able to make one of the most significant EECS discoveries ever. One of the reasons it was likely so difficult for many researchers to come up with the Huffman algorithm was that they were taking their cues from Shannon-Fano codes, which resulted from a rounding estimation of the entropy function. Most researchers investigating this problem (including Shannon) were looking for better, tighter rounding strategies that would somehow result in an optimal code. The idea of this kind of simple greedy algorithm wasn't even on their radar.

Recall that entropy, in the information theory sense as defined below, captures how much information is conveyed on average by a received message. $$H(X) = \sum\limits_{x \in \mathcal{X}}^{} p(x)\cdot\log_2(x)$$ The following code block computes the entropy of the distribution over quantized values in the song compared to the bits/symbol achieved by the Huffman algorithm.

In [ ]:

```
probabilities = freq_dict.values()
probsum = sum(probabilities)
probabilities = probabilities / probsum
entropy = sum([p*np.log2(1/p) for p in probabilities])
bits_per_symbol = # Your implementation here
print "Entropy of Distribution: ", entropy
print "Achieved Bits/Symbol of Huffman Code: ", bits_per_symbol
```

Notice how close the number of bits used per message in the Huffman code is to the true average number of bits conveyed per message in the song! Within 0.04 bits!

As we've seen, source coding is an attempt represent a file as minimally as possible, compressing it to the smallest possible size. When transmitting a file, however, the opposite idea comes into play. We want to increase the redundancy in our file to make sure it gets through correctly, even in the presence of erasures or errors. We will explore attempting to send our Huffman coded audio file across a communication channel.

In general, a communication system is as follows:

You are interested in transmitting a message $m_1,m_2,\ldots$, which in our case will be a binary string representing our compressed audio file. Next, this file will be encoded in some way which adds redundancy to the file. When the encoded message $x_1,x_2,\ldots$ is sent through the communication channel, it is corrupted in some way by a noise source, before being received as output $y_1,y_2,\ldots$. This output is passed through a decoder, which will take advantage of the redundancy added by the encoder in order to accurately reconstruct the file. The decoders estimate for the input message will be $\hat{m}_1,\hat{m}_2,\ldots$, and we would hope that it is the exact same as the input message.

There are dozens of models for different communication channels, but let us look at one in particular: the Binary Symmetric Channel

The binary symmetric channel is a very simple model of a communication channel, and is widely used in probabilistic analysis, information theory, and coding theory. In this model, a transmitter wishes to send a bit (a zero or a one), and the receiver receives a bit. The bit can either be transmitted correctly or can be flipped during tranmission. The "crossover probability" is the probability with which the bit is flipped, which we will denote as $p$. Thus the bit is transmitted correctly with probability $1-p$. A BSC with these properties is depicted below in Figure 2.

Let us take a look at what happens when we try to send our file through this channel without encoding it with any redundancy. In this example we set $p=0.01$.

In [ ]:

```
from random import random
def simulateBSC(message,p):
output = [message[i] if random() > p else str(1-int(message[i])) for i in range(len(message))]
return output
output = simulateBSC(encoded,0.01)
```

In [ ]:

```
decoded = decode_song(output,huff)
```

In [ ]:

```
plt.plot(time[0:120], decoded[0:120]) # Plot the first 0.01 second
plt.title("First 0.01 seconds of decoded version of 'School Bell Ringing'")
plt.xlabel("Time")
plt.ylabel("Amplitude")
```

In [ ]:

```
playMusic('decoded_song', fs, decoded)
```

Your answer here

We know that if we send one physical bit through this channel, the probability of receiving it correctly is $1-p$, and that this is the best we can do (after all, this is the nature of the channel). However, we can do better if we send only one bit of information through the channel by encoding it in more than one physical bit (it is important to note the difference between physical bits and information bits). The most obvious way to do this is through a repetition code. That is, each time we want to send a bit of information, we repeat the bit $r$ times and send them all through the channel. At the receiving end, we use a majority decoding strategy (i.e. decode to whichever symbol, 0 or 1, has been received greater than $\frac{r}{2}$ times).

Before we get to the simulation, let's first do some analysis! We note that $Z \sim B(r,p)$ is a binomially distributed random variable with mean $rp$ and variance $rp(1-p)$. Therefore we can see that the exact probability of decoding error when transmitting one information bit, given some $p$ and $r$ is: Where we are assuming here that $r$ will be such that you never have to make an arbitrary decision (half 1 and half 0). Now, let's try bounding our error using some of the methods we learned in the last lab (we will assume that we want to do better than some fixed probability of error $\epsilon$:

$P(error) = P(Z\geq\frac{r}{2}) \leq \frac{rp}{.5r} = 2p$

$P(error) = P(|Z-rp|\geq \frac{r}{2} - rp)\leq \frac{p(1-p)}{r(\frac{1}{2} - p)^2} = \epsilon$ By rearranging, we obtain $r=\frac{p(1-p)}{\epsilon(\frac{1}{2} - p)^2}$

We will use the CLT in order to obtain an $approximation$ for $r$ that will give you the desired probability of error. We will leave the result in terms of an inverse $\mathcal{Q}$ function: The Normal approximation of the binomial tells us that, approximately, $Z \sim \mathcal{N}\left(rp, rp(1-p)\right)$. Thus in order for the probability of error to go to $\epsilon$, we must have that $P(Z > \frac{r}{2}) = \epsilon$. In order to appropriately use the $\mathcal{Q}$ function, we must transform $Z$ into a standard normal, thus we are looking for $P\left(\frac{Z-rp}{\sqrt{rp(1-p)}} > \frac{\frac{r}{2} - rp}{\sqrt{rp(1-p)}}\right)$. Now we have $$\mathcal{Q}\left(\frac{\frac{r}{2} - rp}{\sqrt{rp(1-p)}}\right) = \epsilon \\ \frac{r(\frac{1}{2} - p)}{\sqrt{r}\sqrt{p(1-p)}} = \mathcal{Q}^{-1}\left(\epsilon\right) \\ \sqrt{r} = \frac{\mathcal{Q}^{-1}\left(\epsilon\right)\sqrt{p(1-p)}}{\frac{1}{2}-p} \\ r = \left(\frac{\mathcal{Q}^{-1}\left(\epsilon\right)\sqrt{p(1-p)}}{\frac{1}{2}-p}\right)^2$$

If you need a starting point, try the following values for $p$: .2, .4. If any line other than the exact probability of error is jagged, you're dealing with an integer effect.

In [7]:

```
import scipy
from scipy import special
rmax = 40;
n = np.array(range(1,rmax+1))
p = .1 #probability of bit flit
a = .5 #proportion of successful bits you need
CLT = # CLT expression here
chebyshev = # Chebyshev expression here
true = # True expression here
plt.semilogy(true)
plt.semilogy(chebyshev)
plt.semilogy(CLT)
plt.legend(['True','Chebyshev','CLT'])
```

In [ ]:

```
import itertools
from __future__ import division
def majority(m):
s = sum(m)
if s > len(m)/2:
return 1
return 0
def BSC_repetition_simulator(message_size, m, r, p):
# Your implementation here
return decoded
message = np.array(map(int,list(encoded)))
trials = 1
message_size = len(message)
reps = 9 #the plots show you that ~10 will suffice
p = .01
error = []
decoded = BSC_repetition_simulator(message_size, message, reps, p)
result = ''.join([str(i) for i in decoded])
playMusic('decoded_song', fs, decode_song(result,huff))
```