KOMPRESI AUDIO LOSSLESS DENGAN MENGGUNAKAN POLINOMIAL LPC (LINEAR PREDICTION CODING) DAN RICE CODING (LOSSLESS AUDIO COMPRESSION USING POLYNOMIAL LPC (LINEAR PREDICTION CODING) AND RICE CODING)

ABSTRAKSI: MP3 merupakan standar de facto kompresi audio saat ini. Hal ini dibuktikan dengan semakin banyaknya perangkat audio player yang mendukung format mp3. Walaupun memiliki rasio kompresi yang kecil, tetapi mp3 memiliki sifat lossy. Artinya ada data yang hilang ketika dilakukan proses kompresi, sehingga jika dilakukan proses dekompresi, hasilnya tidak sama dengan data aslinya. Berdasarkan hal tersebut, maka mulai berkembanglah kompresi audio yang bersifat lossless yang artinya tidak ada data yang hilang ketika proses kompresi dan data hasil dekompresi sama dengan data aslinya. Tahapan yang dilalui dalam proses kompresi audio secara lossless yaitu blocking, inter-channel decorrelation dan entropy coding. Blocking bertujuan untuk membagi sinyal audio ke dalam block-block yang saling berdekatan, interchannel decorrelation bertujuan untuk menghilangkan ketergantungan antar data pada setiap block dengan melakukan dekorelasi antar sampel dalam satu blok, dan entropy coding bertujuan untuk menghilangkan data yang berulang. Tugas Akhir ini membahas penggunaan polynomial LPC (Linear Prediction Coding) dan Rice Coding. Polynomial LPC digunakan pada tahap inter channel deccorelation, sedangkan Rice Coding digunakan pada tahap entropy coding. Setelah dilakukan serangkaian percobaan, terlihat bahwa software LAC memiliki rasio kompresi yang lebih kecil dibandingkan dengan FLAC dan Winzip. Selain itu, output proses dekompresi sama persis dengan input proses kompresi.Kata Kunci : Kompresi, Lossless, Polinomial LPC, Rice Coding.ABSTRACT: Nowadays, the most popular audio format is mp3. It is proved by the increase of audio player which support mp3. It has very small compression ratio, however, mp3 is a lossy compression. Lossy mean there is lost information, so when decompression is done, it is no the same file again. Therefore, lossless compression become an interesting topic, because lossless compression means there is no lost information and the output exactly same as the input. Lossless audio compression commonly has the following stages: blocking, inter-channel decorrelation, and entropy coding. In blocking stage, audio file divided into many contiguous frames. In inter-channel decorrelation stage, the correlation between signals is removed at every frame. Then in entropy coding, the repetitiousness data is removed. This final project explain the using of polynomial LPC and Rice Coding at lossless audio compression. Polynomial LPC used at inter-channel decorrelation, while Rice Coding is used at entropy coding. After compared to FLAC and Winzip, it is shown that LAC has smallest compression ratio among them.Keyword: Compression. Lossless, Polynomial LPC, Rice Coding

A low-delay 8 Kb/s backward-adaptive CELP coder

Code excited linear prediction coding is an efficient technique for compressing speech sequences. Communications quality of speech can be obtained at bit rates below 8 Kb/s. However, relatively large coding delays are necessary to buffer the input speech in order to perform the LPC analysis. A low delay 8 Kb/s CELP coder is introduced in which the short term predictor is based on past synthesized speech. A new distortion measure that improves the tracking of the formant filter is discussed. Formal listening tests showed that the performance of the backward adaptive coder is almost as good as the conventional CELP coder

Mechanical and durability performance of lightweight concrete brick with palm oil fuel ash (POFA)

Lightweight building materials such as precast roof and wall panel has been widely used in the construction industries. This is because lightweight materials could benefits the economy and society in terms of manufacturing, transportation and handling cost. One of the most preferable lightweight material is Expanded Polystyrene (EPS). EPS consist of 98% of air and 2% of polystyrene. Therefore, EPS is very low in density which could contribute in the reduction of building materials mass. Abundance of studies has shown that EPS has significantly contribute to the reduction of brick density. EPS has been used as the aggregates replacement in concrete. However, the existing of EPS in the concrete has reduce the strength performance of the concrete. Due to this, researchers have extend their research in improvising the EPS concrete and brick strength with the addition of pozzolanic materials such as fly ash, rice husk ask, silica fume and etc [1-4]. The ability of these pozzolanic materials in enhancing the strength of brick or concrete has been proven..

Numerical simulation analysis on water jet pressure distribution at various nozzle aperture

The low velocity water jet is required by small scale Unmanned Underwater Vehicle (UUV) to control its position, either to remain statics in its position or to perform a slow and steady locomotion. However, the water jet performance is influenced by the size of nozzle aperture. By studying the pressure distribution around the nozzle area, the water jet velocity could be determined and characterized. In this studies, the ejection pressure was fixed at 23.37 Pa according to the constant actuation. Studies were conducted using ANSYS Fluent software. The results show that the water jet velocity and dynamic pressure are higher for larger nozzle aperture size at constant pressure. The total pressure and dynamic pressure had the lowest pressure drop at certain nozzle aperture size but became constant when the nozzle size was wider. This finding is useful in designing the UUV that powered by contractile water jet thruster

Voice morphing using the generative topographic mapping

In this paper we address the problem of Voice Morphing. We attempt to transform the spectral characteristics of a source speaker's speech signal so that the listener would believe that the speech was uttered by a target speaker. The voice morphing system transforms the spectral envelope as represented by a Linear Prediction model. The transformation is achieved by codebook mapping using the Generative Topographic Mapping, a non-linear, latent variable, parametrically constrained, Gaussian Mixture Model

Source separation techniques applied to linear prediction

The prediction filters are well known models for signal estimation, in communications, control and many others areas. The classical method for deriving linear prediction coding (LPC) filters is often based on the minimization of a mean square error (MSE). Consequently, second order statistics are only required, but the estimation is only optimal if the residue is independent and identically distributed (iid) Gaussian. In this paper, we derive the ML estimate of the prediction filter. Relationships with robust estimation of auto-regressive (AR) processes, with blind deconvolution and with source separation based on mutual information minimization are then detailed. The algorithm, based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics. Experimental results emphasize on the interest of this approach

Application of the method of line prediction coefficients for echo-signal processing for ultrasonic non-destructive testing

At present, the method of linear prediction Coding coefficients is of great interest. Based on this model, systems for the analysis and synthesis of sound signals are built. The use of this model allows you to find differences and features when comparing audio signals. Its important advantage is the relative simplicity of parameter estimation using linear signal processing procedures. In this paper, we presented a method of linear prediction coefficients that allows us to analyze the ultrasonic signal from the point of view of its nonlinearity, which affects its time and frequency characteristics, in order to determine the dependence of the parameters of the method of linear prediction coefficients on the structural-phase state of the material under study - the titanium alloy VT6

A Robust LPC Filtering Method for Time-Resolved Morphology of EEG Activity Analysis

This paper introduces a new time-resolved spectral analysis method based on Linear Prediction Coding (LPC) method that is particularly suited to the study of the dynamics of EEG (Electroencephalogram) activity. The spectral dynamic of EEG signals can be challenging to analyse as they contain multiple frequency components and are often heavily corrupted by noise. Furthermore, the temporal and spectral resolution that can be achieved is limited by the Heisenberg-Gabor uncertainty principle [1]. The method described here is based on a z-plane analysis of the poles of the LPC which allows us to identify and estimate the frequency of the dominant spectral peaks. We demonstrate how this method can be used to track the temporal variations of the various frequency components in a noisy EEG signal