I am working with digital amplifiers and from what I can see there is a DC blocking filter that is placed at the start of the signal chain .My questions are split up as follows :
Why is this DC filter placed at the start of the signal chain ?
2.The cutoff frequency of this filter is just 4Hz Any intuition behind having a cut off as low as 4Hz
This filter is a Double Precision Filter . Any reason why DC blocking should be done with a Double Precision filter rather than a Single Precision Filter ?
Regards
Clauvunate
Related
My goal is to record audio using an electret microphone hooked into the analog pin of an esp8266 (12E) and then be able to play this audio on another device. My circuit is:
In order to check the output of the microphone I connected the circuit to the oscilloscope and got this:
In the "gif" above you can see the waves made by my voice when talking to microphone.
here is my code on esp8266:
void loop() {
sensorValue = analogRead(sensorPin);
Serial.print(sensorValue);
Serial.print(" ");
}
I would like to play the audio on the "Audacity" software in order to have an understanding of the result. Therefore, I copied the numbers from the serial monitor and paste it into the python code that maps the data to (-1,1) interval:
def mapPoint(value, currentMin, currentMax, targetMin, targetMax):
currentInterval = currentMax - currentMin
targetInterval = targetMax - targetMin
valueScaled = float(value - currentMin) / float(currentInterval)
return round(targetMin + (valueScaled * targetInterval),5)
class mapper():
def __init__(self,raws):
self.raws=raws.split(" ")
self.raws=[float(i) for i in self.raws]
def mapAll(self):
self.mappeds=[mapPoint(i,min(self.raws),max(self.raws),-1,1) for i in self.raws ]
self.strmappeds=str(self.mappeds).replace(",","").replace("]","").replace("[","")
return self.strmappeds
Which takes the string of numbers, map them on the target interval (-1 ,+1) and return a space (" ") separated string of data ready to import into Audacity software. (Tools>Sample Data Import and then select the text file including the data). The result of importing data from almost 5 seconds voice:
which is about half a second and when I play I hear unintelligible noise. I also tried lower frequencies but there was only noise there, too.
The suspected causes for the problem are:
1- Esp8266 has not the capability to read the analog pin fast enough to return meaningful data (which is probably not the case since it's clock speed is around 100MHz).
2- The way software is gathering the data and outputs it is not the most optimized way (In the loop, Serial.print, etc.)
3- The microphone circuit output is too noisy. (which might be, but as observed from the oscilloscope test, my voice has to make a difference in the output audio. Which was not audible from the audacity)
4- The way I mapped and prepared the data for the Audacity.
Is there something else I could try?
Are there similar projects out there? (which to my surprise I couldn't find anything which was done transparently!)
What can be the right way to do this? (since it can be a very useful and economic method for recording, transmitting and analyzing audio.)
There are many issues with your project:
You do not set a bias voltage on A0. The ADC can only measure voltages between Ground and VCC. When removing the microphone from the circuit, the voltage at A0 should be close to VCC/2. This is usually achieved by adding a voltage divider between VCC and GND made of 2 resistors, and connected directly to A0. Between the cap and A0.
Also, your circuit looks weird... Is the 47uF cap connected directly to the 3.3V ? If that's the case, you should connect it to pin 2 of the microphone instead. This would also indicate that right now your ADC is only recording noise (no bias voltage will do that).
You do not pace you input, meaning that you do not have a constant sampling rate. That is a very important issue. I suggest you set yourself a realistic target that is well within the limits of the ADC, and the limits of your serial port. The transfer rate in bytes/sec of a serial port is usually equal to baud-rate / 8. For 9600 bauds, that's only about 1200 bytes/sec, which means that once converted to text, you max transfer rate drops to about 400 samples per second. This issue needs to be addressed and the max calculated before you begin, as the max attainable overall sample rate is the maximum of the sample rate from the ADC and the transfer rate of the serial port.
The way to grab samples depends a lot on your needs and what you are trying to do with this project, your audio bandwidth, resolution and audio quality requirements for the application and the amount of work you can put into it. Reading from a loop as you are doing now may work with a fast enough serial port, but the quality will always be poor.
The way that is usually done is with a timer interrupt starting the ADC measurement and an ADC interrupt grabbing the result and storing it in a small FIFO, while the main loop transfers from this ADC fifo to the serial port, along the other tasks assigned to the chip. This cannot be done directly with the Arduino libraries, as you need to control the ADC directly to do that.
Here a short checklist of things to do:
Get the full ESP8266 datasheet from Expressif. Look up the actual specs of the ADC, mainly: the sample rates and resolutions available with your oscillator, and also its electrical constraints, at least its input voltage range and input impedance.
Once you know these numbers, set yourself some target, the math needed for successful project need input numbers. What is your application? Do you want to record audio or just detect a nondescript noise? What are the minimum requirements needed for things to work?
Look up in the Arduino documentartion how to set up a timer interrupt and an ADC interrupt.
Look up in the datasheet which registers you'll need to access to configure and run the ADC.
Fix the voltage bias issue on the ADC input. Nothing can work before that's done, and you do not want to destroy your processor.
Make sure the input AC voltage (the 'swing' voltage) is large enough to give you the results you want. It is not unusual to have to amplify a mic signal (with an opamp or a transistor), just for impedance matching.
Then you can start writing code.
This may sound awfully complex for such a small task, but that's what the average day of an embedded programmer looks like.
[EDIT] Your circuit would work a lot better if you simply replaced the 47uF DC blocking capacitor by a series resistor. Its value should be in the 2.2k to 7.6k range, to keep the circuit impedance within the 10k Ohms or so needed for the ADC. This would insure that the input voltage to A0 is within the operating limits of the ADC (GND-3.3V on the NodeMCU board, 0-1V with bare chip).
The signal may still be too weak for your application, though. What is the amplitude of the signal on your scope? How many bits of resolution does that range cover once converted by the ADC? Example, for a .1V peak to peak signal (SIG = 0.1), an ADC range of 0-3.3V (RNG = 3.3) and 10 bits of resolution (RES = 1024), you'll have
binary-range = RES * (SIG / RNG)
= 1024 * (0.1 / 3.3)
= 1024 * .03
= 31.03
A range of 31, which means around Log2(31) (~= 5) useful bits of resolution, is that enough for your application ?
As an aside note: The ADC will give you positive values, with a DC offset, You will probably need to filter the digital output with a DC blocking filter before playback. https://manual.audacityteam.org/man/dc_offset.html
Bluetooth 5.1 introduced special direction finding signals, where a constant tone extension (CTE) is appended at the end of a certain packet. The CTE itself consists of only digital ones, so the whole CTE is transmitted on the same frequency and same wavelength, which of course boosts the accuracy of the localization.
I have 2 questions about this process and I cannot find answers in literature or Bluetooth specifications:
Having two connected devices A and B, is it possible to do two-way direction finding in a time-division duplex manner.
Example: let's say we configure the CTE exchange to happen over multiple packets, can we do the following:
1 - A sends CTE to B (B estimates the location of A)
2 - B sends CTE to A (A estimates the location of B)
3 - A sends CTE to B (B estimates the location of A)
4 - B sends CTE to A (A estimates the location of B)
and so on?
Does the devices perform frequency hopping during the CTE exchange?
Example: Instead of sending a single CTE on a single frequency (in step 1 and 3 from the previous question), is it possible that A sends multiple CTEs over multiple frequency (Same for device B in steps 2 and 4)?
Any suggestions/information is welcome.
well technically df could be bidirectional. BUT it requires multiple antennas at all receivers.
the CTE enables phase shift detection at the receiver. it 'knows' what the waveform 'should' look like (constant signal value at a known frequency) so it can sample multiple antennas to detect the differences
but no general purpose devices (phones, computers, laptops) have antenna arrays. that is why AoA is the 'easiest' to implement. add a few specialized receivers, and voila!...
currently the antenna array I have for testing is just under 4 inches square.
I don't know the signal processing limits on miniaturizing such arrays while still having them be effective
I wrote some code that takes an audio signal (currently a sine wave) as an input and does the following:
Take frames of n (1024) samples
Apply FFT
Apply iFFT
Play output
With this process the output signal is basically the same as the input signal.
Now, in a second attempt I do:
Take overlapping frames from the input
Apply a window function
FFT
iFFT
Overlap the output frames
In step 1, if I take overlapping frames using a hop size (number of samples to jump to take next frame) of a power of 2 (4, 8, 256...) the output sound is smooth and resembles the original input sound, but with any other hop size, the sound starts to crack down. This happens for any frequency of the input signal. Question 1. Why is the sound smooth only if the hop size is 2^n?.
Currently I use a Hanning window. When the hop size is large (e.g. 512) the output sound has a lower volume than when the hop size is small (e.g.64). This seems an expected behavior, because a small hop-size implies that a sample is reconstructed with more frames, so more signals are added. Question 2. Is there a way to properly scale the output signal so that the volume resembles the original signal?
Thank you!
This should not be happening, Overlap-add method can reconstruct your signal without the problems described, we not know exactly what are you doing, I did it some time ago and its works for any hop size and window size, a small secret is apply zeros after and before your signal to ensure a continuous signal, if you look calmly will realize that your window function works like a fade-in/fade-out if you just concatenate the frames you will notice some clicks or the output signal will look like a vibrato, gets a little tricky to tell where your problem actually find !
Just for Debug, skip the FFT and iFFT steps and see if your signal was correctly constructed, if yes your overlap-add process works, and your problem can be in your FFT/iFFT ...
Overlap-add is normally done without using a non-rectangular window function. Zero-pad instead.
If you do use a window function, then you have to make sure that all the offset window functions sum to a constant level, which for a Von Hann window happens with certain offsets (except at the very beginning or end of the series sum). As
2 - (cos(x)+cos(x+Pi)) == 2
Sum more windows into a result without any scaling, and of course the level of the sum will increase.
I am developing a digital delay on a microcontroller and I am stuck with the delay decay. The delay is implemented with a comb filter.
Here it is: http://www.tonmeister.ca/main/textbook/intro_to_sound_recording837x.png
The delay line, "emulating the tape", is implemented as a circula buffer. The effect can be killed and such case does not represents an issue; when turning the effect off though, I have the tail of the delay left in the buffer to process, as if the delay had been frozen and the tail slowly decay (depending on the feedback gain).
My question is: how many times I have to recirculate samples through the buffer?
One way I thought to approach this could be by modelling the physical process ... assuming that the input sequence has a loudness of 0dB for its entire duration and that, after going through the delay line, it gets attenuated by a factor of 1/10. In terms of loudness this corresponds to a drop of 20dB, as power = voltage^2, every time the sequence goes through the feedback path. The weakest audible sound has a loudness of −130dB but, taking into consideration the ambient noise as well, −120dB will be sufficient as the least reference power. Hence, after the echoes have been through the feedback path 6 times (120dB/20dB) they will be no longer audible.
Is there a more efficient way?
Thank you!
as a software engineer I am facing with some difficulties while working on a signal processing problem. I don't have much experience in this area.
What I try to do is to sample the environmental sound with 44100 sampling rate and for fixed size windows to test if a specific frequency (20KHz) exists and is higher than a threshold value.
Here is what I do according to the perfect answer in How to extract frequency information from samples from PortAudio using FFTW in C
102400 samples (2320 ms) is gathered from audio port with 44100 sampling rate. Sample values are between 0.0 and 1.0
int samplingRate = 44100;
int numberOfSamples = 102400;
float samples[numberOfSamples] = ListenMic_Function(numberOfSamples,samplingRate);
Window size or FFT Size is 1024 samples (23.2 ms)
int N = 1024;
Number of windows is 100
int noOfWindows = numberOfSamples / N;
Splitting samples to noOfWindows (100) windows each having size of N (1024) samples
float windowSamplesIn[noOfWindows][N];
for i:= 0 to noOfWindows -1
windowSamplesIn[i] = subarray(samples,i*N,(i+1)*N);
endfor
Applying Hanning window function on each window
float windowSamplesOut[noOfWindows][N];
for i:= 0 to noOfWindows -1
windowSamplesOut[i] = HanningWindow_Function(windowSamplesIn[i]);
endfor
Applying FFT on each window (real to complex conversion done inside the FFT function)
float frequencyData[noOfWindows][samplingRate/2];
for i:= 0 to noOfWindows -1
frequencyData[i] = RealToComplex_FFT_Function(windowSamplesOut[i], samplingRate);
endfor
In the last step, I use the FFT function implemented in this link: http://www.codeproject.com/Articles/9388/How-to-implement-the-FFT-algorithm ; because I cannot implement an FFT function from the scratch.
What I can't be sure is while giving N (1024) samples to FFT function as input, samplingRate/2 (22050) decibel values is returned as output. Is it what an FFT function does?
I understand that because of Nyquist Frequency, I can detect half of sampling rate frequency at most. But is it possible to get decibel values for each frequency up to samplingRate/2 (22050) Hz?
Thanks,
Vahit
See see How do I obtain the frequencies of each value in an FFT?
From a 1024 sample input, you can get back 512 meaningful frequency-levels.
So, yes, within your window, you'll get back a level for the Nyquist frequency.
The lowest frequency level you'll see is for DC (0 Hz), and the next one up will be for SampleRate/1024, or around 44 Hz, the next for 2 * SampleRate/1024, and so on, up to 512 * SampleRate / 1024 Hz.
Since only one band is used in your FFT, I would expect your results to be tarnished by side-band effects, even with proper windowing. It might work, but you might also get false positives with some input frequencies. Also, your signal is close to your niquist, so you are assuming a fairly good signal path up to your FFT. I don't think this is the right approach.
I think a better approach to this kind of signal detection would be with a high order filter (depending on your requirements, I would guess fourth or fifth order, which isn't actually that high). If you don't know how to design a high order filter, you could use two or three second order filters in series. Designing a second order filter, sometimes called a "biquad" is described here:
http://www.musicdsp.org/files/Audio-EQ-Cookbook.txt
albeit very tersely and with some assumptions of prior knowledge. I would use a high-pass (HP) filter with corner frequency as low as you can make it, probably between 18 and 20 kHz. Keep in mind there is some attenuation at the corner frequency, so after applying a filter multiple times you will drop a little signal.
After you filter the audio, take the RMS or average amplitude (that is, the average of the absolute value), to find the average level over a time period.
This technique has several advantages over what you are doing now, including better latency (you can start detecting within a few samples), better reliability (you won't get false-positives in response to loud signals at spurious frequencies), and so on.
This post might be of relevance: http://blog.bjornroche.com/2012/08/why-eq-is-done-in-time-domain.html