signal-processing

I've a Python code which performs FFT on a wav file and plot the amplitude vs time / amplitude vs freq graphs. I want to calculate dB from these graphs (they are long arrays). I do not want to calculate exact dBA, I just want to see a linear relationship after my calculations. I've dB meter, I will compare it. Here is my code: #!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function import scipy.io.wavfile as wavfile import scipy import scipy.fftpack import numpy as np from matplotlib import pyplot as plt fs_rate, signal = wavfile.read("output.wav") print ("Frequency

I am having some trouble to find or implement an algorithm to find a signal source. The objective of my work is to find the sound emitter position. To accomplish this I am using three vibration sensors. The technique that I am using is multilateration that is based on the time difference of arrival. The time difference of arrival between each sensor are found using Cross Correlation of the received signals. I already implemented the algorithm to find the time difference of arrival, but my problem is more on how multilateration works, it's unclear for me based on my reference, and I couldn't

I want to simulate an interpolator in MATLAB using upsampling followed by a low pass filter. First I have up-sampled my signal by introducing 0's. Now I want to apply a low pass filter in order to interpolate. I have designed the following filter: The filter is exactly 1/8 of the normalized frequency because I need to downsample afterward. (it's a specific excersise to upsample interpolate and downsample in this particular order.) However when I apply this filter to my data using the function filter(myfilter, data) the following signal is generated: I really don't know what is happening to my

I am having trouble wrapping my head how to best replicate some C code in an FPGA using a for-loop (not my first time being stuck on this). The snippet of C code look like this: dot_product(&corr_sum, &sample_data_buffer[sample_index+d_circ_buf_size-sync_pattern_size], &sync_pattern[0], sync_pattern_size); abs_corr_sum += abs(corr_sum); Pretty straightforward, it is taking the dot product of two complex vectors and doing a cumulative sum of it. And he was my attempt to replicate it: always @(sample_index) begin // for each incoming sample abs_corr_sum = 64'd0; corr_sum = 64'd0; for (index2 = 0

I have a repeating signal that varies a little bit with each cycle of a process that repeats roughly every second, though the duration and the contents of each cycle vary from each other a little bit within some parameters. There are a thousand x,y coordinates for every second of my signal data. A small, but important, segment of the data within each cycle is corrupted, and I want to replace each corrupted segment with an upward facing parabola. For each data segment that needs to be replaced by the parabola, I have the x,y coordinates of three points. The vertex/minimum is one of those points

I have an array of samples at 75 Hz, and I want to store them at 128 Hz. If it was 64 Hz and 128 Hz it was very simple, I would just double all samples. But what is the correct way if the samplerates are not a fraction of eachother? When you want to avoid Filtering then you can: handle signal as set of joined interpolation cubics curves but this point is the same as if you use linear interpolation. Without knowing something more about your signal and purpose you can not construct valid coefficients (without damaging signal accuracy) for example of how to construct such cubic look here: my