1.1.        
System’s structure of the determining computer’s malfunctions

Each time the computer
is turned on, the POST diagnostic program that was written into the BIOS chip is
automatically started. The
POST system checks the operability of all the most important components of the
computer: processor, RAM, disk subsystem, system logic (chipset) and all
devices on which the normal functioning of the computer depends. Information
about the results of diagnostics can be given in three ways:

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·      
Sound signals. Each fault corresponds to a series of audio signals that
POST generates during device testing. This method is the main one and the user
need to orient yourself on it. With the help of sound signals, the system
notifies about errors most often.

·      
Text messages. This way POST is used in addition to the audio signals, if
the video system of the computer is in working condition. A message appears
briefly describing the fault and an error code on the screen. The code can be
studied in more detail by using the documentation for the motherboard or the
BIOS. With the help of text messages, the computer only informs about minor
errors.

·      
Hexadecimal codes to a specific port at a specific address. Regardless of
whether audio or text messages are given, the system uses this method. However,
to read hexadecimal codes, you need to have special equipment – a POST card.

If the computer is
working properly and POST testing has completed successfully, you will hear one
short beep, after which the operating system will start to boot. If any
malfunction is detected, the diagnostic program will give a special sound
signal (a sequence of short and long beeps) characterizing the detected error, and
text message if it appears on the screen and the computer will stop working
until the malfunction is eliminated.

1.2.        
Comparison of existing analyzers and identifying conditions for improvement

The main
programs which were found and analyzed during the explanatory work have some
common functions and some differences.  There
were applications such as «??????? BIOS», «Pitidos del BIOS», «BIOS POST codes», «BiosBeepCodes», «Beep
Sounds», «Postcode», «POST-???? BIOS» and others. During analyzing application such as «??????? BIOS» it was determined that
this program contains:

–       
short information how to find out in the computer which kind of BIOS is

–       
3 types of BIOS models beep signals

–       
language – English

–       
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The program «Pitidos del BIOS» contains:

– 6 types of BIOS signal models

– common text messages errors

– standard sound signals errors

– interface language – Spanish

– advertisement

Discover the meaning of the beeps that emit the bios of your computer and
the meaning of the most common screen errors that prevent the computer from
starting normally.

The POST card is an expansion board that has its own digital indicator and
outputs the initialization codes of the motherboard to it. With the latest code
output, you can determine which component has a malfunction. These codes depend
on the BIOS manufacturer of the motherboard. In the absence of errors and
normal passing of the test, POST gives to its indicator a value that does not
change during the operation of the computer, depending on the BIOS version, for
example, on most cards, at the end of the initialization, the code FF is
issued.

The program «BIOS POST codes» contains:

– POST codes

– BIOS error sound signals

– 4 types of BIOS models

– Error descriptions

– filter in searching sound signals and universal password

– description of LED post card indicators

-universal passwords for restoring BIOS

– interface language – English

Beeps The BIOS compiles the error code from the most common computer BIOS
manufacturers so you can fix some hardware errors. Because there are many BIOS
brands, there are no standard signal codes for each BIOS. This app is useful
when you need a quick reference guide.

–       
19 BIOS signal models

–       
Searching system using dropdown list in BIOS manufacturer

–       
Error messages

–       
Error descriptions

–       
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–       
Interface language – English

1.3.        
Comparison of existing sounds recognitions

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Shazam

Shazam uses a smartphone or computer’s built-in microphone to gather a
brief sample of audio being played. It creates an acoustic fingerprint based on
the sample and compares it against a central database for a match. If it finds
a match, it sends information such as the artist, song title, and album back to
the user. Some implementations of Shazam incorporate relevant links to services
such as iTunes, Spotify, YouTube, or Groove Music.

Shazam works by analysing the captured sound and seeking a match based on
an acoustic fingerprint in a database of more than 11 million songs.

Shazam identifies songs based on an audio fingerprint based on a
time-frequency graph called a spectrogram.

Shazam stores a catalogue of audio fingerprints in a database. The user
tags a song for 10 seconds and the application creates an audio fingerprint.

Once it creates the fingerprint of the audio, Shazam starts the search for
matches in the database. If there is a match, it returns the information to the
user; otherwise it returns a “song not known” dialogue.

Shazam can identify prerecorded music being broadcast from any source, such
as a radio, television, cinema or music in a club, provided that the background
noise level is not high enough to prevent an acoustic fingerprint being taken,
and that the song is present in the software’s database.

SoundHound

SoundHound is the free music
discovery app that can listen and identify what’s playing. The music player
gives you full-length songs and videos with real-time song lyrics.

SoundHound includes the
fastest music recognition app that can name tunes from speakers as little as
four seconds. In addition, it also features Geotagging options, Facebook and
Twitter sharing, previews, purchase links, full length YouTube videos and
favorite artists’ top songs and info.

Unlike Shazam, it will hazard
a guess at just about any tune thrown at it, whether it human or
speaker-created, and it’ll succeed most of the time.

There is banner advertising
and is the song ID limit. You get five IDs per month, after which you either
have to purchase five more IDs for $1, or upgrade to SoundHound proper for $5,
which eliminates ads altogether.

Sound Search for Google Play

Sound Search for Google Play – ??? ??????????? ?????????? ?? Google, ??????? ??????? ??? ???????? ????? ????????????? ? ????????? ??????
??????????, ????? ??? ????? ??????????, ??? Soundhound ??? Shazam.

Sound Search for Google Play ????? ????? ? ?????????????. ?? ?????????? ?????? ?? ??????? ???? ??????
?????????? Android, ? ?????? ???, ????? ?? ??????? ?????????????
???????, ??????? ?? ??????, ??? ????? ????? ?????? ?????? ?? ?????? ??????????.
?? ????????? ??????? Sound Search for Google Play ??????? ??? ???????? ????? ?
???????????, ? ????? ???????? ???????. ????? ?????, ?????????? ?????????????
?????? ? Google Store, ??? ?? ?????? ??????? ???
?????????? ??? ? ?????? ??????????.

Sound Search for Google Play – ??? ????? ???????? ??????????. ? ???? ??? ?? ????????????? ???????
????????? ????????????, ??????? ??? ???????, ????????, Shazam ??? Soundhound, ??? ???????? ???????? ? ??????? ???????????, ???????
????? ?? ????? ???? ????????????.

1.4.        
Classifications and methods of sounds recognizing systems

Sound or speech recognition
systems are classified:

•         by the size of the dictionary (a limited set of
words/sounds, a large dictionary);

•         depending on the speaker (speaker-independent and
speaker-independent systems);

•         by type of speech/sound (joint or separate speech/sounds);

•         by appointment (dictation systems, command systems);

•         on the algorithm used (neural networks, hidden Markov
models, dynamic programming);

•         by the size of the dictionary (a limited set of words, a
large dictionary);

•         depending on the speaker (speaker-independent and
speaker-independent systems);

•         by type of speech (joint or separate speech);

•         by appointment (dictation systems, command systems);

•         on the algorithm used (neural networks, hidden Markov
models, dynamic programming);

•         by type of structural unit (phrases, words, sounds,
phonemes, dyphones, allophones);

•         by the principle of selection of structural units (pattern
recognition, selection of lexical elements).

Classification of methods of
speech recognition on the basis of comparison with the standard.

•         Dynamic Time Warping

•         Bayesian discrimination

•         Hidden Markov Model

•         Neural networks

In time series analysis,
dynamic time warping (DTW) is one of the algorithms for measuring similarity
between two temporal sequences, which may vary in speed. For instance,
similarities in walking could be detected using DTW, even if one person was
walking faster than the other, or if there were accelerations and decelerations
during the course of an observation. DTW has been applied to temporal sequences
of video, audio, and graphics data — indeed, any data that can be turned into a
linear sequence can be analyzed with DTW. A well known application has been
automatic speech recognition, to cope with different speaking speeds. Other
applications include speaker recognition and online signature recognition. Also
it is seen that it can be used in partial shape matching application.

In general, DTW is a method
that calculates an optimal match between two given sequences (e.g. time series)
with certain restrictions. The sequences are “warped” non-linearly in
the time dimension to determine a measure of their similarity independent of
certain non-linear variations in the time dimension. This sequence alignment
method is often used in time series classification. Although DTW measures a
distance-like quantity between two given sequences, it doesn’t guarantee the
triangle inequality to hold.

In addition to a similarity
measure between the two sequences, a so called “warping path” is
produced, by warping according to this path the two signals may be aligned in
time. The signal with an original set of points X(original), Y(original) is
transformed to X(warped), Y(original). This finds applications in genetic
sequence and audio synchronisation.

As an example, there are two
time series Q and C, of length n and m respectively, where:

Q = q1,q2,…,qi,…,qn (1)

C = c1,c2,…,cj,…,cm (2)

To align two sequences using
DTW we construct an n-by-m matrix where the (ith,jth) element of the matrix
contains the distance d(qi,cj) between the two points qi and cj (With Euclidean
distance, d(qi,cj) = (qi – cj)2). Each matrix element (i,j) corresponds to the
alignment between the points qi and cj. This is illustrated in Figure 4. A
warping path W, is a contiguous (in the sense stated below) set of matrix
elements that defines a mapping between Q and C. The kth element of W is
defined as wk = (i,j)k so it will be:

W = w1, w2, …,wk,…,wK max(m,n)
? K

x

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