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

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

Sparsity in Linear Predictive Coding of Speech

nrpages: 197status: publishe

Speech Recognition dengan Ekstraksi Fitur Linear Predictive Coding dan JST Cerebellar Model Articulation Controller

ABSTRAKSI: Speech recognition merupakan teknologi yang memanfaatkan sinyal suara manusia sebagai masukan untuk dikenali oleh system dan kemudian akan dimanfaatkan untuk berbagai keperluan. Dalam prosesnya sinyal akustik suara yang ditangkap oleh microphone diubah kedalam bentuk sinyal digital. Pada tugas akhir ini, ekstraksi ciri suara menggunakan metode LPC. LPC mengambil ciri-ciri yang ada dalam sampel-sampel suara manusia dan kemudian menjadi masukan untuk proses pembelajaran JST CMAC. CMAC adalah salah satu jenis jaringan syaraf tiruan yang berusaha meniru pola kerja otak belakang manusia. CMAC akan mengasosiasikan setiap masukan kedalam vektor asosiasi dengan menggunakan generator alamat. Generator alamat ini yang akan mengaktifkan sel-sel bobot jaringan. Dari hasil pengujian diperoleh performansi sistem LPC-CMAC rata-rata sebesar diatas 80% untuk data testing.Kata Kunci : Speech Recognition, Jaringan Syaraf Tiruan, LPC, CMACABSTRACT: Speech recognition is a technology that utilizes the human voice as an input signal to be recognized by the system and then be used for various purposes. In the process the sound of acoustic signals captured by the microphone is converted into digital signal form. In this final project, the voice feature extraction using LPC methods. LPC take on the characteristics in samples of human voice and then be input to the CMAC neural network learning process. CMAC is a kind of artificial neural networks that try to mimic the pattern behind the human brain works. CMAC will associate each input vector into an association by using the address generator. This address generator that will activate cells of the network weights. From the test results obtained LPC-CMAC system performance by an average of above 80% for data testing.Keyword: Speech Recognition, Artificial Neural Network, LPC, CMAC

Drone Detection using Audio Analysis

Drones used for illegal purposes is a growing problem and a way to detect these is needed. This thesis has evaluated the possibility of using sound analysis as the detection mechanism. A solution using linear predictive coding, the slope of the frequency spectrum and the zero crossing rate was evaluated. The results showed that a solution using linear predictive coding and the slope of the frequency spectrum give a good result for the distance it is calibrated for. The zero crossing rate on the other hand does not improve the result and was not part of the final solution. The amount of false positives increases when calibrating for longer distances, and a compromise between detecting drones at long distances and the number of false positives need to be made in the implemented solution. It was concluded that drone detection using audio analysis is possible, and that the implemented solution, with linear predictive coding and slope of the frequency spectrum, could with further improvements become a useable product

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

Speech Recognition of Isolated Arabic words via using Wavelet Transformation and Fuzzy Neural Network

In this paper two new methods for feature extraction are presented for speech recognition the first method use a combination of  linear predictive coding technique(LPC) and skewness equation. The second one(WLPCC) use a combination of linear predictive coding technique(LPC),  discrete wavelet transform(DWT), and cpestrum analysis. The objective of this method is to enhance the performance of the proposed method by introducing more features from the signal. Neural Network(NN) and Neuro-Fuzzy Network are used in the proposed methods for classification. Test result show that the WLPCC method in the process of features extraction, and the neuro fuzzy network in the classification process had highest recognition rate for both the trained and non trained data. The proposed system has been built using MATLAB software and the data involve ten isolated Arabic words that are (الله، محمد، خديجة، ياسين، يتكلم، الشارقة، لندن، يسار، يمين، أحزان), for fifteen male speakers. The recognition rate of trained data is (97.8%) and non-trained data  is (81.1%). Keywords: Speech Recognition, Feature Extraction, Linear Predictive Coding (LPC),Neural Network, Fuzzy networ