This document provides an introduction to audio processing using microcontrollers, explaining concepts such as sound as a mechanical wave and the role of loudspeakers in converting electrical signals to sound. It discusses sampling theory, specifically the Nyquist theorem, and how to convert analog sounds into digital formats like .wav files using pulse code modulation. Additionally, it outlines steps to analyze audio data with tools like Audacity and highlights limitations of microcontroller memory in handling audio samples.

1. HOW TO PLAY AUDIO FROM A
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Mahadev G
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MICROCONTROLLER

2. INTRODUCTION TO SOUND
• In physics, sound is a vibration that propagates as a typically audible mechanical wave of
pressure and displacement, through a medium such as air or water.[1]
• So sound is a collection of
compression and
rarefaction of pressure of a
single or varying frequencies
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3. ELECTRONIC SOUND - SPEAKER
• A loudspeaker (or loud-speaker or speaker) is an electroacoustic transducer; a device
which converts an electrical audio signal into a corresponding sound.[2]
• The sound as an electrical signal is a collection of waves of different frequencies, but
audible sound(for humans) is an electrical signal having frequencies in [20Hz,20kHz] ; but
peak audible frequencies lie between [2kHz,5kHz]
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4. • So sound is pretty much an analog signal; which is an infinite data set. For the signal to be
understood by microcontrollers it needs to be digital and finite. To make it finite we must
sample the sound with an interval so that the information of the analog signal is not lost.
• We’ll go to Nyquist theorem for this, so for a signal not be aliased (corrupted) the minimum
sampling frequency should be 2*Fbw, where Fbw is the maximum frequency component present
in the signal.
• So lets analyse a song.
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5. SPECTRUM OF “IN THE END”– LINKIN PARK
• Spectrum Curtsy Audacity
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6. • So looking at the spectrum of the song, there is an attenuation of -30dB at 4kHz and a -37dB
attenuation at 8kHz, after that the attenuation just increases. The more the attenuation the
less we hear.
From this we can infer that a song has less of high frequency components that we actually
hear.
• Most of the sound we hear are under the 8kHz band.
• So using our good old Nyquist sampling theorem a sampling frequency of 8kHz*2 would be
good enough to reproduce the song.[or any audible sound for that matter]
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7. SAMPLED AT 16KHZ
• So looking at the spectrum of the
16kHz sampled song, you find that
the core sound of the song is
retained without loss of information
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8. HOW SOUND IS SAVED..
• Now that the sound has become finite, we
need to save it. Each sample has an analog
value/amplitude associated to it. This has to
be captured, this is done by ADC’s and
saved as 8,16 or 32 bit data depending upon
the quality of the sound.
• So putting it all together, we have for each
second of a song we have 16000 8/16/32 bit
data. This is how a .wav file is represented.
This kind of modulation is called pulse code
modulation.
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9. HOW TO RECONSTRUCT THE ANALOG
SIGNAL FROM THE DIGITAL AUDIO SAMPLES
• Since each sample stored represents the amplitude of the signal at that particular instant,
we will use Pulse Width modulation to recreate the signal amplitude.
• Why pulse width modulation??
• It’s the easiest way to produce an analog voltage from a digital signal.
• How?
• Explained better in the link http://arduino.cc/en/Tutorial/PWM
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10. INTERPOLATING..
• Now you have at t=1/16000 having amplitude x1 and t=2/16000 having amplitude x2, to
make the signal continuous the space between these two times should have something !!
• So you flood it with a PWM signal having an average of x1 from time time t=1/16000 to
t=2/16000, so that the average between these two sample times is x1, and similarly
between t=2/16000 to t=3/16000 with a PWM signal of average x2.
• For that your PWM frequency should be higher than 16kHz. Choose a frequency 10 times
greater so that at least 10 waves can fit in between two adjacent samples.
• Choosing a higher frequency for the PWM signal can greatly improve the noise
characteristics of your audio since the more the frequency of the PWM signal the more
the noise is being pushed out of the frequency band we are using. [ How?? See last slide]
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11. THE NUTSHELL..
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12. DO WE NEED A LOW PASS FILTER AT THE END?
• Actually the low pass filter is not required as if
you look at the equivalent circuit of the standard
loud speaker, you see that the R1 and C1 forms
an LPF
• But an Amplifier might be required if you are
trying to drive powerful loudspeakers directly
using a microcontroller output pin.
• For a standard headphone the microcontroller
pin drive strength is good enough to reproduce
sound with good volume.
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13. HOW TO GET THE AUDIO DATA FROM A SONG..
1. Install the open source app “Audacity”
http://audacity.sourceforge.net/
2. Open any song in it, and export it as
.wav file (screen shot).
3. Reopen the .wav file
4. Goto Analyse menu-> Sample data
Export
You have a lot of handles from there to
export, get the signal amplitude in numbers
or in dB scale; export to csv or txt file format
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14. THE ALGORITHM IN THE
MICROCONTROLLER..
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15. LIMITING FACTORS
• The major limiting factor is definitely the code memory of the microcontroller, Each
sample takes a minimum of 8bit of space, and since we have 16k samples in a second we
have 16k * 8 bits of space for a second. Which is sort of the max limit for most
microcontrollers available under 3$.
• Using a sampling frequency of 8k would double the amount of sound that you could play
without compromising much on quality. But if your sounds are very low frequency even 6k
would work, but would not recommend using sub 8k frequencies for this application.
• So if you want to play a whole song interface an SRAM or SPI-Flash.
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16. THANKS AND COURTESY
• Hugely helping in understanding sound, modulation, reconstruction and noise is the
application note by Audio Precision.
http://users.ece.utexas.edu/~bevans/courses/rtdsp/lectures/10_Data_Conversion/AP_Underst
anding_PDM_Digital_Audio.pdf
• Please go through this as it will be an eye opener.
• Audacity
• Wikipedia for all definitions [1] [2] and google image search for images ;)
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