42 results about "Linear prediction coding" patented technology

Provided are a method and apparatus for effectively encoding/decoding remaining difference signals excluding sinusoidal components, from input audio signals. In the method and apparatus for encoding audio signals, sinusoidal analysis is performed on low frequency signals of less than a predetermined critical frequency in order to extract sinusoidal signals and then, an encoding operation is performed on the remaining difference signals excluding the sinusoidal signals, from input audio signals, by using linear prediction coding (LPC) analysis.

A voice encoding/decoding method and apparatus. A voice encoder includes: a quantization selection unit generating a quantization selection signal; and a quantization unit extracting a linear prediction coding (LPC) coefficient from an input signal, converting the extracted LPC coefficient into a line spectral frequency (LSF), quantizing the LSF with a first LSF quantization unit or a second LSF quantization unit based on the quantization selection signal, and converting the quantized LSF into a quantized LPC coefficient. The the quantization selection signal selects the first LSF quantization unit or second LSF quantization unit based on characteristics of a synthesized voice signal in previous frames of the input signal.

The related echo eliminator comprises: a linear predicted decoder to decode data into remote speech signal and extract the short activating signal and linear prediction coding parameter, a decorrelation filter to receive the predicted coding parameter and error signal for decorrelation and generate decorrelated error signal, an adaptive filter to receive all of the remote speech and short activating signal and decorrelated error signal and generate the estimated echo, and a subtracter to generate the error signal. This invention combines with LPC device, reduces algorithm complexity, and prompts algorithm convergence.

The invention relates to an echo canceller, an echo cancellation method and the double-end talking detection system thereof, wherein, the echo canceller decodes a data stream from the network end into a far-end voice signal and extracts a short-time excitation signal as well as a linear prediction coding coefficient by utilizing a linear prediction decoder. Based on the linear prediction coding coefficient, the echo canceller carries out a decorrelation process to a residual signal by a decorrelation filter, and executes a normalized cross correlation process to the short-time excitation signal and the decorrelation residual signal by utilizing the double-end talking detector to generate the normalized cross correlation detection variable, hence, the echo canceller detects the existence of the double-end talking and then further controls the renewal of the self-adaptive filter. The invention is capable of detecting the existence of the double-end talking by the decorrelation process as well as the normalized cross correlation process to the far-end voice signal and the residual signal, thus obviously decreasing the calculation complexity of the double-end talking detection and the storage space consumption.

Provided are methods and apparatuses for more efficiently encoding and decoding a high frequency band signal which is from an audio signal and which is greater than a predetermined threshold frequency. The method and apparatus for encoding the audio signal encodes a linear prediction coding (LPC) coefficient and gain information of a residual signal, which are generated by performing LPC analysis, thereby encoding a high frequency signal so as to have enhanced sound quality, while using less bits.

An apparatus for processing an encoded signal and method thereof are disclosed, by which an audio signal can be compressed and reconstructed in higher efficiency. An audio signal processing method includes the steps of identifying whether a coding type of the audio signal is a music signal coding type using first type information, if the coding type of the audio signal is not the music signal coding type, identifying whether the coding type of the audio signal is a speech signal coding type or a mixed signal coding type using second type information, if the coding type of the audio signal is the mixed signal coding type, extracting spectral data and a linear predictive coefficient from the audio signal, generating a residual signal for linear prediction by performing inverse frequency conversion on the spectral data, reconstructing the audio signal by performing linear prediction coding on the linear predictive coefficient and the residual signal, and reconstructing a high frequency region signal using an extension base signal corresponding to a partial region of the reconstructed audio signal and band extension information. Accordingly, various kinds of audio signals can be encoded/decoded in higher efficiency.

Audio coding and decoding apparatuses and methods that can optimize the quality of an audio signal including harmonics, and recording mediums storing the methods. An audio coding apparatus includes: a first harmonic coding module performing first harmonic coding on an input audio signal using a pitch lag of the input audio signal and producing a quantized linear prediction coding coefficient; a first detector detecting a first difference audio signal from a difference between an audio signal output from the first harmonic coding module and the input audio signal; a second harmonic coding module performing harmonic coding on the first difference audio signal using the quantized linear prediction coding coefficient and a previous harmonic coding result; a second detector detecting a second difference audio signal obtained from a difference between an audio signal output from the second harmonic coding module and the first difference audio signal; and a code excited linear prediction (CELP) module CELP coding the second difference audio signal using the quantized linear prediction coding coefficient obtained from the first harmonic coding module.

The invention discloses an electrocardiogram signal lossless compression method based on PT conversion and linear prediction combination, belonging to the technical field of data processing. The method comprises the following compression processes: carrying out PT conversion on an original electrocardiogram signal; subtracting a converted original signal value and an estimated value to obtain a residual error signal of the whole electrocardiogram signal; carrying out self-adaption variable grade RICE coding on the residual error signal to output a coding bit stream and finishing the lossless compression on the electrocardiogram signal; decoding the compressed bit stream; reconstructing the signal and finishing decompression. The lossless compression method ensures the information completion and accuracy of the compressed electrocardiogram signal and can be used for transmitting and storing the electrocardiogram signal.

Systems, methods, and apparatus for the detection of signals having spectral peaks with narrow bandwidth are described herein. The range of described configurations includes implementations that perform such detection using parameters of a linear prediction coding (LPC) analysis scheme.

The invention discloses a method for changing voice by means of speech tone modification on the basis of derivative glottal flow models. The method has the advantages that tone-modified speech can besynthesized by the aid of linear prediction coding technology and derivative glottal flow combined processes, residual signals can be obtained from speech signals by the aid of LPC (linear predictioncoding) inverse filters, are first-order difference of glottal signals, and are simulated by LF models in detail to obtain high-quality glottal excitation signals, and accordingly high-quality tone-modified speech can be synthesized; the method is simple, effective and speedy and has extensive practical value, and voice sources can be flexibly controlled by te/tc and tp/tc.

Disclosed are a method for coding a residual signal of LPC coefficients based on collaborative quantization and a computing device for performing the method. The residual signal coding method includes: generating encoded LPC coefficients and LPC residual signals by performing LPC analysis and quantization on an input speech; Determining a predicted LPC residual signal by applying the LPC residual signal to cross module residual learning; Performing LPC synthesis using the coded LPC coefficients and the predicted LPC residual signal; It may include the step of determining an output speech that is a synthesized output according to a result of performing the LPC synthesis.

The present invention relates to communication technologies and discloses a method, an apparatus and a system for Linear Prediction Coding (LPC) analysis to improve LPC prediction performance and simplify analysis operation. The method includes: obtaining signal feature information of at least one sample point of input signals; comparing and analyzing the signal feature information to obtain an analysis result; selecting a window function according to the analysis result to perform adaptive windowing for the input signals and obtain windowed signals; and processing the windowed signals to obtain an LPC coefficient for linear prediction. The embodiments of the present invention are applicable to LPC.

The invention relates to a widely applicable mother tongue monosyllable identification system which comprises a previous processor, a characteristic matrix method in which the known monosyllable sound wave normalization is consistent with the selected size, wherein, an elastic frame is adopted to normalize the sound wave and transfer the sound wave into linear prediction coding cepstrum characteristic matrixes in equal size and the same known monosyllable sound wave is transferred into matrixes with the same characteristics; to each known monosyllable, K best samples are chosen; and the K best samples of one known monosyllable characteristic matrix are transferred into a standard model and stored in a database. The standard model comprises the mean value and the variance number of the K best samples of the known monosyllable characteristic matrix; the unknown monosyllable sound wave is normalized and transferred into a characteristic matrix which has the same size with the known standard model, includes the linear prediction coding ceptrum inside, and is called as an unknown monosyllable classifying model; the unknown monosyllable classifying model are compared with all the known monosyllable standard model in the database to find a known monosyllable, which has the smallest distance with the unknown monosyllable and is identified as an unknown monosyllable.