关于音频采集的外文文献

An Android-Based Uncertainty Digital Audio Representation for Frequency Analyzer Determining Various Frequencies of Digital Signal of The Sound
Pandan Pareanom Purwacandra Department of Informatics Engineering
STMIK AMIKOM
Yogyakarta, Indonesia
e-mail: pandan_pianis@
Ferry Wahyu Wibowo Department of Informatics Engineering
STMIK AMIKOM
Yogyakarta, Indonesia
e-mail: ferrywahyuwibowo@
Abstract—The digital audio analyzer based on the smartphone is used to measure the strength of the sound frequency that is purposed in the a udio field ca ptured in the gra phic ba r. The fast track of frequency is to be analyzed and determined by the gra phic form displa y. In this pa per, the a na log signa l da ta ca ptured by microphone of sma rtphone tha t is embedded operating system of android. The operating system of android is suita ble for development of product to test ha rmony level both embedded hardware and embedded software. Feasibility errorness could be emerging in the input, filtering, arbitrary, a nd output processes. In this ca se, the good sensor a nd quite lgorithms must ha ve more portion to be identified a nd defined to get result a s well. But, due to a good sensor isn’t much showing in this pa per. This pa per shows how getting noise of digital data, glitch, can affect in the output display and which solution should be ta ken to countera ct it by a lgorithm a nd coding. The result of processes level of digita l a udio is deriva ted by bina ry da ta from eva lua ting da ta produced by digita l-to-a na log converter (DAC) which is embedded in the smartphone.
Keywords-analyzer; android; audio; digital; frequency; sound
I.I NTRODUCTION
A lot of problems in the frequency area couldn’t be solved by modelling, but along with it, many approaches proposed to solute the problems. This research is made to contribute on one of sound frequency analyzer working on the android operating system that will ease users to take application benefits of mobile devices. Frequency analysis is needed to predict the propability of the graphic bar of the captured-sound to be matched for specific needs with general reference e.g. 6d
B rules and pink guide. Android operating system was chosen because it has high flexibilities and supports many types of smartphone categories, so the application is more widely to be explored.
A sound generated by osilation of surrounding object that made the air environment oscillated in the range of human heared frequency i.e. 20 Hz to 20,000 Hz. In other words, sound of 20 Hz frequency has oscillate 20 times per second. The types of frequencies could be defined by frequency analyzer to capture the frequency respon of sound and it could be divided into 3 types. The digital audio has 3 types of frequency i.e. high, mid, and low. In addition, to capture a sound in analog data and convert it into digital data can be done by sampling technology. The sampling technique used
to capture sound sample in second that is called sample rate, the common rate of sampled sound is about 44,100 Hz. Although, the maximum of human hearing is about 20 kHz, the low of capturing sampling can emerge an aliasing and an unfiltered as well [1]. Audio coding algorithms are used to get the representation of wideband audio signal efficiently being transmission, in other word, how to transmit and store least bits of audio signal [2]. Representations of wideband audio including multichannel audio need bandwidths of at least 20kHz. The conventional digital format of digital audio
is pulse-code modulation (PCM) that has sampling rates of 32, 44.1, or 48 kHz and an amplitude resolution (PCM bits per sample) of 16 bit. The multiple coding can reveal source distorsions and that’s way, lossless audio coding has become an issues [3]. In this paper, the sampling rate is taken by coding and used to analysis of buffering digital data.
Soft computing is the collection field of learning and cognitive ability and less error of uncertainty and imprecision that consists of fuzzy logic, neural computing, evolutionary computation, machine learning and probabilistic reasoning. Soft computing has been implemented in various applications, even in the multimedia data (video, image, audio, text, color, etc.) is one of these applications [4]. A classification of system architectures for uncertainty calculation based on fuzzy system was done by [5] for improvement of detection, fusion and aggregation of context knowledge using microphone of mobile phone. Measurements were adapted from mobile phone sensors i.e. microphone and acceleration sensory. The detection of sound applied to recognize various applause and non-applause and the sampled-data processed using recurrent fuzzy classifier
in combination with uncertainty measures.
A process of capturing sound source, an analog data, recorded in analyzer device consists of few steps that are sound captured by microphone which is embedded in the android smartphone. The microphone is used to be converted analog sound signal, an electrical voltage, into digital data by analog-to-digital converter (ADC). In this case, a surrounding object oscillates air environment to move microphone diaphragm forward and backward in the small scale. The binary digital data will be processed by fast fourier transform (FFT) algorithm and converted into graphic bar in the display screen of smartphone. This process is worked on mobile operating system of android. Android is a
mobile operating system suitable for developing and engineering that was derivated from an operating system of linux and open source-based. An android have many versions.
II.
R ELATED W ORKS
The formula for the speed of sound c 0 in air is expressed by (1).
[U .0
0p c
(1)
Where c 0 is the speed of sound in air at 0 o C (celcius degree), p 0 is an atmospheric air pressure of 101,325 Pa (pascal), U 0 is a density of air at 0 o C the value is about 1.293 kg.m -3, and [is an adiabatic index of air at 0 o C, the value is about 1.402. So, from the equation 1 the speed of sound c 0 in air at 0 o C is resulted a value by (2).
10
0.5,331402.1293
.1325
,101.
s m x p c [U (2)
The simplified formula of the speed of sound, c , is expressed by (3).
10..1 s m c c V K (3)
The symbol of K expresses an expansion coefficient of 1/273.15=3.661x10-3 in 1/o C. In addition, the value of -273.15 o C is equal to 0 o K (Kelvin). The symbol of V expresses a temperature in unit of o C. Usually, to get the simplest formula of the speed of sound, c , can be written by (4).
1..60.05.331 s m c V (4)
The expression of (4) shows that the speed of sound c is only dependent on the temperature V . In this paper presents a various places for the object samples where are having various temperature measurements, i.e. at the office, the generator set environment, the street, and the highway. Due to the speed of sound, the classification of frequencies can be defined by (5).
Hz c
f O (5) The frequency of f in Hertz (Hz) is the ratio of the speed of sound and the wavelength of O in metre (m). A digital audio system works by measurin
g that is called
sampling. The principle of sampling is to get value of voltage level of an analog signal at a time, then the value of
samples converted into digital data. The stream of representative words can be stored in a form that represents
the source analog signal. The stored data can then be
processed and reproduced to yield an audio production. The sampling time is the elapsed time that occurs between each sampling period e.g. a sample rate of 44.1 kHz corresponds
to a sample time 1/44,100 of a second. The sampling rate of a system determines its overall bandwidth, which is system with higher sample rates is capable of storing more frequencies at its upper limit. According to the Nyquist theorem, in order to get desired frequency bandwidth to be faithfully encoded in the digital domain, the selected sample rate must be at least twice of highest frequency to be recorded, meaning sample rate > 2 x highest frequency. So, an audio signal with a bandwidth of 44.1 kHz would require that the sampling rate be at least 88.2 kHz samples/second. That is very important to get no audio signal greater than half the sampling frequency enter into the digital conversion process. If frequencies greater than the sample rate, then it will emerge erroneous frequencies, known as alias frequencies and the sound might be heard as harmonic distortion (see fig. 1). Generally, to eliminate the effects of aliasing, a low-pass filter is placed before analog-to-digital
conversion [6].
Figure 1. Digitization process above the Nyquist half-sample frequency
and alias frequencies introduced into audio band [6].
Some audio analyzers are designed to measure conventional analog amplifiers. A conventional analog amplifier not only amplifies the input signal, but it also adds harmonics that result from its nonlinearities. In order to reach bandwidth limiting filter, the design of the input block of the audio analyzers can be a problem. Fast fourier transform (FFT) measurement is always used to conjuction with post-processing to calculate the desired parameter value. Such processed values to find the 20-20,000 Hz root mean square (RMS) noise (V (rms,noise)) for an amplifier running with no signal, an FFT analysis with a length of 16K samples, a sampling frequency of 44,1 kHz and Blackman-Harris window is performed, and post-processing can be written as some steps those are perform the FFT analysis to achieve a
noise floor measurement, thus based on the FFT, calculation
of the V
(rms, noise) for the FFT parameters give a bin width
shown by (6)[7]. bin Hz length FFT frequency FFT /69165.21638444100)
()
( (6) For the target band bin (integer) values shown by (7) and (8).
7/69165.220| bin Hz Hz Startbin (7)
7430/69165.2000.20| bin
Hz Hz
Stopbin (8) Calculation of the RMS noise in the target band, taking the FFT window energy loss shown by (9).
¦ 74307
2
)
,()(),(.
n n bin ection WindowCorr noise rms V
K V (9)
Correction factors for various types of windows are given in table 1.
TABLE I.
C ORRECTION F ACTORS FOR V ARIOUS FFT W INDOW
T YPES [7]
FFT Window Type K(WindowCorrection)
Blackman-Harris
0.7610
Hanning 0.8165 Hamming 0.8566
Bartlet 0.8660 No window
1.0000
The wideband receivers the signal bandwidth is composed of many individual channel, and a single ADC is
used to digitize the entire bandwidth. Process gain in dB unit is due to oversampling of sampling frequency, f s , and inversely to twice of bandwidth, the equation shown by (10). BandWidth
s f ocessGain .210log 10Pr (10) Application of fuzzy logic in the artistic and creative fields are not abundant because the techniques of artificial intelligent have been used widely purposes. Two applications of fuzzy logic in the artistic domain can be formed to a fuzzy logic-based mapping strategy for audiovisual composition and a novel audio synthesis technique based on sound particles and fuzzy logic [8]. In the fuzzy system both inputs and outputs are classified and de-classified into fuzzy sets. A fuzzy rules are computed in parallel to produce an outputs, thus those outputs are de-fuzzyfied to meet the desired variables. In this paper presents
the types of frequencies to be fuzzyfied, so the recognized waves are obtained at the such circumstances.
III.
E XPERIMENT S UPPORT AND T OOLS
A.Mobile Sensing
A sensor of data audio that is embedded in mobile phone has a problem in its authenticity of the data. Although in that case, experiments with a prototype implementation for android can be done. An audio analyzer having trade-off between both the performance and power cost, and having noticeable effect on interactive applications and central processing unit (CPU)-intensive analysis completing asynchronously in under 70 seconds for 5-minute audio clips [9]. Analog input sources are converted by sensor into voltages and currents. The electrical quantities can emerge as fast or slow direct current continuous measurements of a
phenomenon in the time domain, as modulated alternating current waveforms, or in some combination, with spatial configuration of related variables to represent shaft angles. The voltages are normalized to ranges compatible with ADC input work-range [7]. In this paper discusses about the audio analyzer based on android is applicable in some circumstances to get the accuracy of capturing frequencies and ignoring the quality of the sensor device (i.e. microphone that is embedded in the smartphone).
In this paper gets the environment temperatures of office is about 25 o C and the generator set, street, and highway are about 32o C. So, the speed of sound of each places can be resumed by table II.
TABLE II. S PEED OF S OUND IN V ARIOUS P LACES
Environments Speed of Sound
(m.s -1)
Office
346.5
Generator Set 350.7 Street 350.7
Highway 350.7 From the experiments of sound frequencies, in this paper
observes that the frequencies of various situations has never
stopped waving in the graphic bars as shown in fig. 2. Noises
occur more widely at any situations at the office, generator set, street, and highway environments. The display of narrow
frequency is made by condensed environment and the oscillation in such systems. (a) (b)(c)
(d)
Figure 2. The results of experiment on digital audio analyzer of sound frequencies at (a) the office (b) the generator set environment (c) the street
(d) the highway.
B.Reasoning Techniques on Sounds
In this paper, the frequency is divided into three definitions, i.e. high, middle, and low. The range of high frequency is about 4096 to 32768 Hz. A high frequency gives briliance and presence of audio. The range frequency of 5,000 to 10,000 Hz is known as sibilance which is
affected by ‘S’ character from saying. A range middle frequency is around of 1024 to 2048 Hz that can be found from human speech intelligibility which gives a tinny quality to sound. And the last, a low frequency at the range of 16 to 512 Hz has human threshold of feeling.
In this paper also implements pink noise to be object reviewed. Pink noise is a signal with a frequency spectrum such the power spectral density (power per Hz) is inversely proportional to the frequency. At the pink noise, each octave carries an equal amount of noise power that the pink appearance of visible light with this power spectrum. The term of 1/f noise is sometimes used to refer any noise with a power spectral density. Noises occur widely at any situations even in the office, generator set, street, and highway environments. The display of narrow frequency is made by condensed environment and the oscillation in such systems. In this paper also implements pink noise to be object reviewed. Pink noise is a signal with a frequency spectrum such the power spectral density (power per Hz) is inversely proportional to the frequency. At the pink noise, each octave carries an equal amount of noise power that the pink appearance of visible light with this power spectrum. The term of 1/f noise is sometimes used to refer any noise with the power spectral density. The pink noise fuzzied and defined by high, middle, and low. A fuzzy logic is a novel approach to get applicable pattern in the implementation. The specific class of audio patterns can be heared by human and the device just recognizes such various frequencies of sounds of the pink noise.
IV.C ONCLUSIONS
An android-based digital audio analyzer has been made succesfully to caught various frequencies at the office, generator set, street, and highway environments. In this paper also presents a pink noise to be tested in the application as sound sources, so the measurement can result the differ of various frequencies. Pink noise has all of the frequency from low to high and it is devided into three types low, mid, and high frequencies. The processing sound is used to elaborate frequency sampling using FFT algorithm and displayed on the graphic bar screen of android smartphone.
A CKNOWLEDGMENT
We thank to the God who give chance to be involved in this research. We also wish to thank STMIK AMIKOM Yogyakarta has funded our research. We hope our least contribution in this knowledge can be meaning much more to the future researchs.
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