An encoder for encoding a parametric spectral representation (f) of auto-regressive coefficients that partially represent an audio signal. The encoder includes a low-frequency encoder configured to quantize elements of a part of the parametric spectral representation that correspond to a low-frequency part of the audio signal. It also includes a high-frequency encoder configured to encode a high-frequency part (f.sup.H) of the parametric spectral representation (f) by weighted averaging based on the quantized elements ({circumflex over (f)}.sup.L) flipped around a quantized mirroring frequency ({circumflex over (f)}.sub.m), which separates the low-frequency part from the high-frequency part, and a frequency grid determined from a frequency grid codebook in a closed-loop search procedure. Described are also a corresponding decoder, corresponding encoding/decoding methods and UEs including such an encoder/decoder.

An encoder for encoding a parametric spectral representation (f) of auto-regressive coefficients that partially represent an audio signal. The encoder includes a low-frequency encoder configured to quantize elements of a part of the parametric spectral representation that correspond to a low-frequency part of the audio signal. It also includes a high-frequency encoder configured to encode a high-frequency part (f.sup.H) of the parametric spectral representation (f) by weighted averaging based on the quantized elements ({circumflex over (f)}.sup.L) flipped around a quantized mirroring frequency ({circumflex over (f)}.sub.m), which separates the low-frequency part from the high-frequency part, and a frequency grid determined from a frequency grid codebook in a closed-loop search procedure. Described are also a corresponding decoder, corresponding encoding/decoding methods and UEs including such an encoder/decoder.

An encoding method includes determining a target adaptive broadening factor based on a quantized line spectral frequency (LSF) parameter of a primary channel signal in a current frame and an LSF parameter of a secondary channel signal in the current frame, and writing the quantized LSF parameter of the primary channel signal in the current frame and the target adaptive broadening factor into a bitstream.

An encoding method includes determining a target adaptive broadening factor based on a quantized line spectral frequency (LSF) parameter of a primary channel signal in a current frame and an LSF parameter of a secondary channel signal in the current frame, and writing the quantized LSF parameter of the primary channel signal in the current frame and the target adaptive broadening factor into a bitstream.

Systems and methods for training a machine-learned model are provided. A method can include can include obtaining an unlabeled audio signal, sampling the unlabeled audio signal to select one or more sampled slices, inputting the one or more sampled slices into a machine-learned model, receiving, as an output of the machine-learned model, one or more determined characteristics associated with the audio signal, determining a loss function for the machine-learned model based at least in part on a difference between the one or more determined characteristics and one or more corresponding ground truth characteristics of the audio signal, and training the machine-learned model from end to end based at least in part on the loss function. The one or more determined characteristics can include one or more reconstructed portions of the audio signal temporally adjacent to the one or more sampled slices or an estimated distance between two sampled slices.

Systems and methods for training a machine-learned model are provided. A method can include can include obtaining an unlabeled audio signal, sampling the unlabeled audio signal to select one or more sampled slices, inputting the one or more sampled slices into a machine-learned model, receiving, as an output of the machine-learned model, one or more determined characteristics associated with the audio signal, determining a loss function for the machine-learned model based at least in part on a difference between the one or more determined characteristics and one or more corresponding ground truth characteristics of the audio signal, and training the machine-learned model from end to end based at least in part on the loss function. The one or more determined characteristics can include one or more reconstructed portions of the audio signal temporally adjacent to the one or more sampled slices or an estimated distance between two sampled slices.

An encoding method includes determining an adaptive broadening factor based on a quantized line spectral frequency (LSF) vector of a first channel of a current frame of an audio signal and an LSF vector of a second channel of the current frame, and writing the quantized LSF vector and the adaptive broadening factor into a bitstream.

An encoding method includes determining an adaptive broadening factor based on a quantized line spectral frequency (LSF) vector of a first channel of a current frame of an audio signal and an LSF vector of a second channel of the current frame, and writing the quantized LSF vector and the adaptive broadening factor into a bitstream.

A spectrum coding method includes quantizing spectral data of a current band based on a first quantization scheme, generating a lower bit of the current band using the spectral data and the quantized spectral data, quantizing a sequence of lower bits including the lower bit of the current band based on a second quantization scheme, and generating a bitstream based on a upper bit excluding N bits, where N is 1 or greater, from the quantized spectral data and the quantized sequence of lower bits.

An encoder and a method therein for Pyramid Vector Quantizer, PVQ, shape search, the PVQ taking a target vector x as input and deriving a vector y by iteratively adding unit pulses in an inner dimension search loop. The method comprises, before entering a next inner dimension search loop for unit pulse addition, determining, based on the maximum pulse amplitude, maxamp.sub.y, of a current vector y, whether more than a current bit word length is needed to represent enloop.sub.y, in a lossless manner in the upcoming inner dimension loop. The variable enloop.sub.y is related to an accumulated energy of the vector y. The performing of this method enables the encoder to keep the complexity of the search at a reasonable level.