Parov Stelar – The Princess (2. New. Album. Releases. Artist: Parov Stelar. Album: The Princess. Released: 2. 01. 2Style: Electronic. Format: MP3 3. 20. Kbps / FLACSize: 1. Mb. CD1: 0. 1 – Milla’s Dream. All Night. 03 – Nobody’s Fool. The Princess. 05 – Silent Shuffle. This Game. 07 – You Got Me There. Dust in the Summer Rain. Requiem for Annie. With You. 11 – The Beach. Song for the Crickets. Beautiful Morning. The Fog. 15 – True Romance Part 2 (Bonus Track)CD2: 0. Jimmy’s Gang. 02 – Sally’s Dance. Booty Swing. 04 – Baska Brother. The Phantom. 06 – The Snake. Wanna Fete. 08 – Oh Yeah. Parov Stelar Album: The Princess Released: 2012. Posted on April 10th, 2012 at 5:52 pm. I entered and bought his CD Single collection. The musical blog IsraBox is a huge collection of musical albums, discographies of groups and artists. Listen and download tracks and albums. Exclusive music of all genres from around the world. The Vamp. 10 – The Paris Swing Box. All Night (Extended Club Version)1. A Song for You. 13 – Jimmy’s Gang (Enzo Siffredi Remix) (Bonus Track)DOWNLOAD LINKS: CD1: RAPIDGATOR: DOWNLOADCD2: RAPIDGATOR: DOWNLOADFLAC (2. Mb): CD1: RAPIDGATOR: DOWNLOADCD2: RAPIDGATOR: DOWNLOADPreviously on New. Album. Releases. net.
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CHAPTER 2. Before design a voice enhancement system, we have to understand the behaviour of various types of noise, difference between the noise source and the range of noise levels that may be encountered in real life. Generally noise refers to the anything that interferes with what we want. There are many technical challenges trying to extract a desired information signal from a background of unwanted noise such as in building a cell phone system, robust voice recognition, ultrasound machine and others. Noise Sources. In real life, noise will surround us wherever we go. It will appear in different shapes and forms. For example, in the street the cars are passing by and people talking at the nearby table in the restaurant. Generally, noise can be divided into two types which are stationary and non- stationary. Stationary noise has a spectral power density that does not change over time, for example noise make by a computer fan or air conditioning. While the non- stationary noise has statistical properties that change over time, for example noise generated caused by door slam, radio, voice and TV. We can clearly make a conclusion where the task of suppressing non- stationary noise is more difficult than that of suppressing stationary noise. Another feature of the various type of noise is the shape of spectrum, the distribution of noise energy in the frequency domain. By compare the long term average spectra example of noise sources in restaurant, car and train where the noise sources were taken from the NOIZEUS corpus. We found that the car noise is relatively stationary but the restaurant and train noise are not. The noise sources are more distinct in frequency domain rather than the time domain. Normally, there are two main sources of distortion may make the voice signal degraded which are additive noise and channel distortion. For the additive noise, it can be categorised into stationary and non- stationary as explain as above statement. For example, a fan running in the background, a door slams and a conversation among others. If we captured the signal with the speaker close to the microphone for sure the signal captured has a little noise and reverberation. However, if the microphone is far from the speaker's mouth it can pick up a lot of noise and reverberation. For channel distortion, it can be caused by reverberation such as the frequency response of a microphone, the response of the local loop of a telephone line, the presence of an electrical filter in an ADC circuit and a speech codec. The reverberation is caused by the reflection of acoustic waves on the walls and other objects in the room that will alter the speech signal. For the direct path, the signal level at the microphone is inversely proportional to the distance from the speaker. For the reflected sound waves, the signal level is inversely proportional with the distance of sound travel. Besides that, we have to take into account the energy absorption which takes place each time the sound wave hits a surface where the different surface material will give a differently absorption. Noise and Speech Levels in Various Environments. To the design of speech enhancement algorithms, we have to understanding the knowledge of the range of speech and noise intensity levels in real- world scenarios in order to estimate the range of signal- to- noise ratio (SNR) levels encountered in realistic environment. The speech enhancement algorithms have to work effectively in suppressing noise and hence to improving speech quality within that range of SNR levels. In 1. 97. 7, Pearson and colleagues (Pearson, Bennett & Fidell, 1. They considered a variety of environments encountered in daily life such as outside and inside of home, commuter train, nursing station and department store. This analysis had provided an important corpus of data on typical speech and noise, SNR ratio across a wide variety of everyday listening situations. The speech and noise levels were measured using sound levels meters. The measurements were reported in d. B sound pressure level (SPL) where the d. B SPL is the relative pressure of sound in reference to 0. From the figure, we found that the speech levels are increase in extremely high background noise levels. Generally, people will tend to raise their voices when noise level goes beyond 4. EPA Document Collection. Behavioral and Physiological Correlates of Varying Noise Environments Abstract PDF. C Combatting Noise in the 90's. A Noise Workbook PDF Speech Levels in Various Noise. Environmental noise is the summary of noise. Noise at low levels is not. However, the adverse effects of noise exposure (i.e. Protective Noise Levels. B SPL, this phenomenon is known as the Lombard effect. The speech level will tends to increase 0. B for every 1d. B increase in background noise. When the ambient noise level goes beyond 7. B SPL, people will stop raising their voice. In practical environment, the speech enhancement algorithms to be employed have needs to operate at SNRs in the range of 0- 1. B. Speech Perception and modelling. Speech Perception. Processes by which humans are able to interpret and understand the sounds used in the language are called speech perception. Normally, the speech perception is closely linked to the phonology and phonetic field. Researches for seek to understand how the humans recognize the speech sounds and use this information to understand the spoken language were done by many speech perception researchers. These researches about the speech perception have been used in some applications in the construction of computer systems which are able to recognize the speech and transmit a meaning signal, as well as to improve the recognition for hearing of listeners. Figure below the shows speech production of human's organ, there are a lot of biological and psychological factors which can affect the speech which include disorders with the lungs, the larynx and the vocal cords. Figure 2. 2 Speech production of human's organ. Engineering Model of Speech Production. Nowadays, a lot of electronic devices which can use the voice that as similar as possible to a real human voice and speak to us. Figure shown below is the one of the production voice model. Figure 2. 3 Source- tract model of speech production. Reason for using this model is because this model has been used extensively for low- bit- rate speech coding application. By using this model, firstly we have to decide the noise that we want to produce is voiced or unvoiced. Voiced sounds are produced when the vocal folds are in the voicing state where unvoiced sounds are produced when the vocal folds are in the unvoicing state. If we want the unvoiced sounds the signal produced which is sound like noise we can see in the fricative sounds. In this model, vocal tract resonance is represented by a quasi- linear system that is excited by either a periodic or aperiodic sources, depending on the state of the vocal folds. The vocal folds can assume one of two states is modelled by a switch. The vocal tract is modelled by a time- invariant linear filter. The vocal tract resonance will filter the signal with a filter that tries to mimic the effect of the shape formed with the pharyngeal cavity (throat), vocal and nasal cavity. Lastly, the output of vocal tract filter is fed to the radiation model. The radiation model will reproduce the effect of the radiation impedance that the air put up to the exit of the speech from the mouth. Algorithms for Voice Enhancement. Recently, voice processing algorithms can roughly be divided into three domains, spectral subtraction, sub- space analysis and filtering algorithms. Spectral subtraction algorithms operate in the spectral domain by removing the amount of energy which corresponds to the noise contribution from each spectral band. Spectral subtraction is one of the popular algorithms being used in speech enhancement because it is work effectively in estimating the spectral magnitude of the voice signal. Sub- space analysis operates in the autocorrelation domain. The voice and noise components can be assumed to be orthogonal whereby their contributions can be readily separated but to find the orthogonal components is computationally expensive. Besides that, the orthogonal assumption is difficult to motivate. Therefore, this algorithm is not encouraged to use in our project. Another algorithm which is filtering algorithms is operating in time- domain which includes Wiener filtering and Kalman filtering. That Wiener filtering attempt to either remove the noise component and Kalman filtering approach to estimate the noise and voice components. Spectral- Subtractive Algorithms. The spectral subtraction algorithm is historically one of the first algorithms proposed for noise reduction (Boll, 1. Weiss et al., 1. 97. Based on the principle, we assuming it are additive noise and the noise spectrum can be estimated and updated during the periods when the signal is absent. Then we obtain an estimate of the clean signal spectrum by subtracting out the estimated of the noise spectrum from the noisy speech spectrum. The enhanced signal is obtained by computing the inverse discrete Fourier transform (IDFT) of the estimated signal spectrum using the phase of the noisy signal. This algorithm involves a single forward and inverse Fourier transform. To avoid any speech distortion we have to carefully during the subtraction process. If too little is subtracted then the speech signal remains will interfere with noise. If too much is subtracted then some part of speech information might be removed. Principle of Spectral Subtraction. The principle of spectral subtraction shown below is introduced by Boll in 1. Let y(n) be the sampled noisy speech signal, x(n) be the clean signal and d(n) be the noise signal. We assume the sampled noisy speech signal consist clean signal and noise signal, therefore we can write asy(n) = x(n) + d(n) (1)Taking the short- time Fourier transform of y(n), we get. Y (. If we assume that d(n) is zero mean and uncorrelated with the clean signal x(n), then the terms E. Thus, from the above assumptions, the estimate of the clean speech power spectrum, denoted as . The estimated power spectrum . Dn. B 3. 65: July 2. 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