147,218 research outputs found
Low bit rate speech coding methods and a new interframe differential coding scheme for line spectrum pairs
Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1992.Thesis (Master's) -- Bilkent University, 1992.Includes bibliographical references leaves 30-32.Low bit rate speech coding techniques and a new coding scheme for vocal
tract parameters are presented. Linear prediction based voice coding techniques
(linear predictive coding and code excited linear predictive coding) are
examined and implemented. A new interframe differential coding scheme for
line spectrum pairs is developed. The new scheme reduces the spectral distortion
of the linear predictive filter while maintaining a high compression ratio.Erzin, EnginM.S
Linear predictive coding of speech
Linear Predictive Coding (LPC) is a mathematical (signal processing) technique used to remove the redundancy from speech signals. To remove the redundancy from speech signal, is done in 1\vo stages, first stage is to remove short term correlations form the signal and the second is to
remove long term correlations. The short term and long term redundancies are modelled by digital filters very precisely. This chapter is dedicated for LPC ofspeec
Codebook excited linear predictive coding
In standard Codebook Exited Linear Coding (CELP) the encoding method uses random values for excitation vectors. The excitation is modelled by the codebook. Each time a segment of speech is encoded and an excitation vector is matched to minimise the error between original and
encoded signal to maintain quality of speech. This chapter narrates the CELP encoding algorithm. The conceptual block diagram of twoO time varying filters and a Gaussian codebook
shown in figures, fc encoder and decoder respectively. This simplified block diagram mainly consists ofthree blocks, Short-Term Prediction, Long-Term Prediction and a random code-book. The parameters of these predictors SIP, LIP and the codebook are optimised and estimated in many ways. If these estimated parameters are accurate, the synthesized speech \vilI sound the same as original speech. Because ofthe limitations ofthe coding build blocks the estimated filter parameters case as the estimation errors in a result speech quality suffers degradations. The standard CELP algorithm explained gradually as follows and its block diagram shown in figur
On the Sample Complexity of Predictive Sparse Coding
The goal of predictive sparse coding is to learn a representation of examples
as sparse linear combinations of elements from a dictionary, such that a
learned hypothesis linear in the new representation performs well on a
predictive task. Predictive sparse coding algorithms recently have demonstrated
impressive performance on a variety of supervised tasks, but their
generalization properties have not been studied. We establish the first
generalization error bounds for predictive sparse coding, covering two
settings: 1) the overcomplete setting, where the number of features k exceeds
the original dimensionality d; and 2) the high or infinite-dimensional setting,
where only dimension-free bounds are useful. Both learning bounds intimately
depend on stability properties of the learned sparse encoder, as measured on
the training sample. Consequently, we first present a fundamental stability
result for the LASSO, a result characterizing the stability of the sparse codes
with respect to perturbations to the dictionary. In the overcomplete setting,
we present an estimation error bound that decays as \tilde{O}(sqrt(d k/m)) with
respect to d and k. In the high or infinite-dimensional setting, we show a
dimension-free bound that is \tilde{O}(sqrt(k^2 s / m)) with respect to k and
s, where s is an upper bound on the number of non-zeros in the sparse code for
any training data point.Comment: Sparse Coding Stability Theorem from version 1 has been relaxed
considerably using a new notion of coding margin. Old Sparse Coding Stability
Theorem still in new version, now as Theorem 2. Presentation of all proofs
simplified/improved considerably. Paper reorganized. Empirical analysis
showing new coding margin is non-trivial on real dataset
Reconciling Predictive Coding and Biased Competition Models of Cortical Function
A simple variation of the standard biased competition model is shown, via some trivial mathematical manipulations, to be identical to predictive coding. Specifically, it is shown that a particular implementation of the biased competition model, in which nodes compete via inhibition that targets the inputs to a cortical region, is mathematically equivalent to the linear predictive coding model. This observation demonstrates that these two important and influential rival theories of cortical function are minor variations on the same underlying mathematical model
KLASIFIKASI SUARA PARU-PARU MANUSIA MENGGUNAKAN LINEAR PREDICTIVE CODING DAN LINEAR DISCRIMINANT ANALYSIS
ABSTRAKSI: Secara klinis, salah satu cara yang digunakan dokter untuk mendiagnosis penyakit sistem pernapasan adalah dengan melakukan analisa suara paru-paru manusia menggunakan stetoskop. Hasil diagnosa dokter sangat tergantung pada kepekaan telinga dan pengalaman yang bersangkutan, frekuensi dan amplitudo yang rendah, serta pola suara yang relatif sama.Pada tugas akhir ini, ada sembilan kelas data suara paru manusia yang diklasifikasikan yaitu bronchial, bronchovesikular, vesikular, tracheal, asthma, coarse crackle, fine crackle, grunting, dan wheeze, di mana masing-masing kelasnya memiliki lima data suara. Metode yang digunakan pada tugas akhir ini adalah teknik Linear Predictive Coding, Short Time Fourier Transform untuk ekstraksi ciri, dan Linear Discriminant Analysis sebagai classifier.Pada sistem klasifikasi suara paru-paru manusia menggunakan metode LPC dan LDA, tingkat akurasi data yang diperoleh adalah sebesar 93,33%. Hal ini menunjukkan bahwa LDA dapat digunakan sebagai salah satu metode pengklasifikasian suara paru-paru manusia dengan tingkat performansi yang baik.Kata Kunci : bronchial, bronchovesikular, vesikular, tracheal, asthma, coarse crackle, fine crackle, grunting, wheeze, linear predictive coding, short time fourier transform, linear discriminant analysis.ABSTRACT: Clinically, one way that doctors use to diagnose diseases of the respiratory system is a sound analysis of human lungs using a stethoscope. The results of the doctor\u27s diagnosis is highly dependent on the sensitivity of the ear and experience is concerned, the low frequency and amplitude, and the sound patterns that are relatively similar.At the end of this task, there are nine classes of voice data that is classified human lung bronchial, bronchovesicular, vesikular, tracheal, asthma, coarse crackle, fine crackle, grunting, and wheeze, in which each class has a five-voice data. The method used in this thesis is Linear Predictive Coding techniques, Short Time Fourier Transform for feature extraction, and Linear Discriminant Analysis as a classifier.In the human lung sound classification system using LPC and LDA, the accuracy of the data obtained is equal to 93.33%.This demonstrates that the LDA can be used as a method of classification of human lung sounds with a good level of performance.Keyword: bronchial, bronchovesicular, vesikular, tracheal, asthma, coarse crackle, fine crackle, grunting, wheeze, linear predictive coding, short time fourier transform, linear discriminant analysis
Software Pengenalan Pola Clustering Huruf Menggunakan Metode Linear Predictive Coding dengan Visual Basic 6.0.
Tujuan dari pembuatan proyek akhir ini adalah 1). Dapat membuat
Software Pengenalan Pola Clustering Huruf vokal Menggunakan Metode Linear
Predictive Coding (LPC) dengan Visual Basic 6.0 dalam dunia nyata. 2)
Mengetahui unjuk kerja Software Pengenalan Pola Clustering Huruf vokal
Menggunakan Metode Linear Predictive Coding dengan Visual Basic 6.0.
Software Pengenalan Pola Clustering Huruf Menggunakan Metode Linear
Predictive Coding dengan Visual Basic 6.0 merupakan penerapan algoritma
LPC sebagai solusi dibidang pendidikan dengan menggunakan mikrofon yang
dihubungkan dengan perangkat komputer. Mirofon sebagai alat untuk merekam
suara yang akan dijadikan sebagai input dan akan menjadi masukan perangkat
komputer. Software Pengenalan Pola Clustering Huruf Menggunakan Metode
Linear Predictive Coding dengan Visual Basic 6.0 dapat dibuat dengan
menggunakan bahasa pemrograman Visual Basic 6.0 yang digabungkan dengan
mikrofon sebagai instrumen pendukung yang akan dikendalikan atau
diperintahkan oleh perangkat komputer untuk merekam dimana dalam Project
Visual Basic 6.0 perlu diberi tambahan Components berupa Microsoft Multimedia
Control 6.0, dan dengan pengkodean dari Visual Basic 6.0 dibuat algoritma LPC
sebagai extraksi file suara.
Hasil yang diperoleh berupa Software Pengenalan Pola Clustering Huruf
vokal Menggunakan Metode Linear Predictive Coding (LPC) dengan Visual
Basic 6.0 dengan obyek tambahan pada Visual Basic 6.0 yaitu Microsoft
Multimedia Control 6 yang berfungsi sebagai perekam dan penyimpan file suara
yang mempunyai format WAV, PCM, 8kHz, 8 Bit, mono. Tugas Akhir ini
menggunakan koefisien pada pemrosesan LPC sebagai berikut a = 15/16, N =
240, M = 80 dan P = 10. Hasil dari proses terakhir yaitu analisa LPC yang berupa
koefisien LPC digambarkan pada obyek Picture1 menjadi kumpulan titik – titik
yang membentuk cluster yang berbeda antara pengucapan 1 huruf vokal dengan
huruf vokal yang lain. Terdapat perbedaan juga antara penggambaran suara nada
rendah yang berbentuk relatif bulat dengan penggambaran suara nada tinggi yang
berbentuk lebih oval atau memanjang.
Kata Kunci : Linear Predictive Coding, Clustering, Pengenalan Pola Suara
- …