hammingWindows.py

import matplotlib.pyplot as plt
import numpy as np
import asyncio

import acquisition as acq
#-------------------------------------------
#            Hamming Window Implementation
#-------------------------------------------
#This part was required because there's need to change 1D speech array into 2D array

FRAME_SIZE = 0.02 #0.02 means 20 ms frames
dt = 9.07029E-5


async def execute():
    frameLength = int(int(FRAME_SIZE / dt) * 0.75)
    forN = int((((len(acq.data["filtered"]) * dt) / FRAME_SIZE * 4) - 1 ) / 3 )
    #Conclusion: forN is doubled because when we use doubled frequency acquisition

    for i in range(forN):
        #If you want to plot overall speech and results of Hamming window, comment 2 next lines of code
        acq.data["currHamming"] = []
        acq.data["windowedHamming"] = []
        currIdx = frameLength * i
        for j in range(currIdx, (currIdx + frameLength)):
            acq.data["currHamming"].append(acq.data["filtered"][j])
        window = np.hamming(len(acq.data["currHamming"])) 
        acq.data["windowedHamming"] = window * acq.data["currHamming"] #1D array
        acq.data["framesMatrix"].append(acq.data["windowedHamming"]) #2D array
    
async def plot():
    plt.figure()
    plt.plot(acq.data["currHamming"], label='Last Speech Frame')
    plt.plot(acq.data["windowedHamming"], label='Hamming Windowed')
    plt.title("Implementation of Hamming Window")
    plt.ylabel("Amplitude")
    plt.xlabel("Sample")
    plt.legend()
    plt.show()

#print(framesMatrix)