The invention provides an audio encoder including a combination of a linear predictive coding filter having a plurality of linear predictive coding coefficients and a time-frequency converter, wherein the combination is configured to filter and to convert a frame of the audio signal into a frequency domain in order to output a spectrum based on the frame and on the linear predictive coding coefficients; a low frequency emphasizer configured to calculate a processed spectrum based on the spectrum, wherein spectral lines of the processed spectrum representing a lower frequency than a reference spectral line are emphasized; and a control device configured to control the calculation of the processed spectrum by the low frequency emphasizer depending on the linear predictive coding coefficients of the linear predictive coding filter.

The invention provides an audio encoder including a combination of a linear predictive coding filter having a plurality of linear predictive coding coefficients and a time-frequency converter, wherein the combination is configured to filter and to convert a frame of the audio signal into a frequency domain in order to output a spectrum based on the frame and on the linear predictive coding coefficients; a low frequency emphasizer configured to calculate a processed spectrum based on the spectrum, wherein spectral lines of the processed spectrum representing a lower frequency than a reference spectral line are emphasized; and a control device configured to control the calculation of the processed spectrum by the low frequency emphasizer depending on the linear predictive coding coefficients of the linear predictive coding filter.

Methods and apparatus to perform audio watermarking and watermark detection and extraction are disclosed. Example apparatus disclosed herein are to select frequency components to be used to represent a code, different sets of frequency components to represent respectively different information, respective ones of the frequency components in the sets of frequency components located in respective code bands, there being multiple code bands and spacing between adjacent code bands being equal to or less than the spacing between adjacent frequency components in the code bands. Disclosed example apparatus are also to synthesize the frequency components to be used to represent the code, combine the synthesized frequency components with an audio block of an audio signal, and output the audio signal and a video signal associated with the audio signal.

Methods and apparatus to perform audio watermarking and watermark detection and extraction are disclosed. Example apparatus disclosed herein are to select frequency components to be used to represent a code, different sets of frequency components to represent respectively different information, respective ones of the frequency components in the sets of frequency components located in respective code bands, there being multiple code bands and spacing between adjacent code bands being equal to or less than the spacing between adjacent frequency components in the code bands. Disclosed example apparatus are also to synthesize the frequency components to be used to represent the code, combine the synthesized frequency components with an audio block of an audio signal, and output the audio signal and a video signal associated with the audio signal.

A device and a method for quantizing a LPC filter in the form of an input vector in a quantization domain, comprises a calculator of a first-stage approximation of the input vector, a subtractor of the first-stage approximation from the input vector to produce a residual vector, a calculator of a weighting function from the first-stage approximation, a warper of the residual vector with the weighting function, and a quantizer of the weighted residual vector to supply a quantized weighted residual vector. A device and a method for inverse quantizing of a LPC filter, comprises means for receiving coded indices representative of a first-stage approximation of a vector representative of the LPC filter in a quantization domain and of a quantized weighted residual version of the vector, a calculator of an inverse weighting function from the first-stage approximation, an inverse quantizer of the quantized weighted residual version of the vector to produce a weighted residual vector, a multiplier of the weighted residual vector by the inverse weighting function to produce a residual vector, and an adder of the first-stage approximation with the residual vector to produce the vector representative of the LPC filter in the quantization domain.

A device and a method for quantizing a LPC filter in the form of an input vector in a quantization domain, comprises a calculator of a first-stage approximation of the input vector, a subtractor of the first-stage approximation from the input vector to produce a residual vector, a calculator of a weighting function from the first-stage approximation, a warper of the residual vector with the weighting function, and a quantizer of the weighted residual vector to supply a quantized weighted residual vector. A device and a method for inverse quantizing of a LPC filter, comprises means for receiving coded indices representative of a first-stage approximation of a vector representative of the LPC filter in a quantization domain and of a quantized weighted residual version of the vector, a calculator of an inverse weighting function from the first-stage approximation, an inverse quantizer of the quantized weighted residual version of the vector to produce a weighted residual vector, a multiplier of the weighted residual vector by the inverse weighting function to produce a residual vector, and an adder of the first-stage approximation with the residual vector to produce the vector representative of the LPC filter in the quantization domain.

An audio signal encoding and decoding method using a learning model, a training method of the learning model, and an encoder and decoder that perform the method, are disclosed. The audio signal decoding method may include extracting a first residual signal and a first linear prediction coefficient by decoding a bitstream received from an encoder, generating a first audio signal from the first residual signal using the first linear prediction coefficient, generating a second linear prediction coefficients and a second residual signal from the first audio signal, obtaining a third linear prediction coefficient by inputting the second linear prediction coefficient into a trained learning model, and generating a second audio signal from the second residual signal using the third linear prediction coefficient.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O(i) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O(i) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

Provided are a system and method for modifying a speech recognition result. The method includes: receiving, from a device, text output from an automatic speech recognition (ASR) model of the device; identifying at least one domain related to the received text; selecting, from among a plurality of text modification models included in the server, at least one text modification model corresponding to the identified at least one domain; and modifying the received text by using the selected at least one text modification model.