reachsumit

reachsumit

# We will use wave package available in native Python installation to read and write .wav audio file
import wave
# read wave audio file
song = wave.open("song.wav", mode='rb')
# Read frames and convert to byte array
frame_bytes = bytearray(list(song.readframes(song.getnframes())))

# The "secret" text message
string='Peter Parker is the Spiderman!'
# Append dummy data to fill out rest of the bytes. Receiver shall detect and remove these characters.

reachsumit

# Use wave package (native to Python) for reading the received audio file
import wave
song = wave.open("song_embedded.wav", mode='rb')
# Convert audio to byte array
frame_bytes = bytearray(list(song.readframes(song.getnframes())))

# Extract the LSB of each byte
extracted = [frame_bytes[i] & 1 for i in range(len(frame_bytes))]
# Convert byte array back to string
string = "".join(chr(int("".join(map(str,extracted[i:i+8])),2)) for i in range(0,len(extracted),8))

reachsumit

;nyquist plug-in
;version 1
;type process
;name "Subliminal..."
;action "Subliminal..."
;control carrier "Carrier" real "Hz" 17500 14000 20000

(setf carrier (max 14000 (min carrier 20000)))

;; We have two Nyquist frequencies, carrier/2 and *sound-srate*/2.

reachsumit

;; replace the below frequency number with the original frequency used to embed the secret
(mult *track* (hzosc 17500.0))

reachsumit

import io
import fasttext

def load_vectors(fname):
    fin = io.open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore')
    n, d = map(int, fin.readline().split())
    data = {}
    for line in fin:
        tokens = line.rstrip().split(' ')
        data[tokens[0]] = map(float, tokens[1:])

reachsumit

import io
import fasttext

def load_vectors(fname):
    fin = io.open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore')
    n, d = map(int, fin.readline().split())
    data = {}
    for line in fin:
        tokens = line.rstrip().split(' ')
        data[tokens[0]] = map(float, tokens[1:])

reachsumit

import numpy as np
import pandas as pd

from numpy import bincount, log, log1p
from scipy.sparse import coo_matrix, linalg

class ExplicitCF:
    def __init__(self):
        self.df = pd.read_csv("ml-100k/u.data", sep='\t', header=None, names=['user', 'item', 'rating'], usecols=range(3))
        self.df['user'] = self.df['user'].astype("category")

reachsumit

import numpy as np
import pandas as pd

from numpy import bincount, log, log1p
from scipy.sparse import coo_matrix, linalg

class ImplicitCF:
    def __init__(self):
        self.df = pd.read_csv("lastfm-dataset-360K/usersha1-artmbid-artname-plays.tsv", sep='\t', header=None, names=['user', 'artist', 'plays'], usecols=[0,2,3])
        self.df['user'] = self.df['user'].astype("category")

reachsumit