A signal processing method and device includes obtaining spectral coefficients of a current frame of an audio signal, in which N sub-bands of the current frame comprises at least one of the spectral coefficients. A total energy of M successive sub-bands of the N sub-bands, a total energy of K successive sub-bands of the N sub-bands, and an energy of a first sub-band are obtained to determine whether to modify original envelope values of the M sub-bands. When the original envelope values of the M sub-bands are modified, encoding bits are allocated to each of the N sub-bands according to the modified envelope values of the M sub-bands.

The coding efficiency of an audio codec using a controllable—switchable or even adjustable—harmonic filter tool is improved by performing the harmonicity-dependent controlling of this tool using a temporal structure measure in addition to a measure of harmonicity in order to control the harmonic filter tool. In particular, the temporal structure of the audio signal is evaluated in a manner which depends on the pitch. This enables to achieve a situation-adapted control of the harmonic filter tool so that in situations where a control made solely based on the measure of harmonicity would decide against or reduce the usage of this tool, although using the harmonic filter tool would, in that situation, increase the coding efficiency, the harmonic filter tool is applied, while in other situations where the harmonic filter tool may be inefficient or even destructive, the control reduces the appliance of the harmonic filter tool appropriately.

The invention relates to the technical field of microphones, in particular to an online trimmed MEMS microphone. The online trimmed MEMS microphone comprises an acoustic transducer for receiving an external ultrasonic signal and converting the ultrasonic signal into an electric signal; an ASIC (Application Specific Integrated Circuit) chip, coupled to the acoustic transducer, wherein the ASIC chip comprises: an amplifier unit for performing amplification on the electric signal and outputting an amplified signal; a decoding unit, connected to the amplifier unit, and configured to decode the amplified signal to obtain a decoded signal sequence; a matching unit, connected to the decoding unit, and configured to match the decoded signal sequence with a predetermined identification code to obtain a matched signal; a control unit, connected to the matching unit, and configured to generate one or more circuit adjusting parameters under the action of the matched signal.

The invention relates to the technical field of microphones, in particular to an online trimmed MEMS microphone. The online trimmed MEMS microphone comprises an acoustic transducer for receiving an external ultrasonic signal and converting the ultrasonic signal into an electric signal; an ASIC (Application Specific Integrated Circuit) chip, coupled to the acoustic transducer, wherein the ASIC chip comprises: an amplifier unit for performing amplification on the electric signal and outputting an amplified signal; a decoding unit, connected to the amplifier unit, and configured to decode the amplified signal to obtain a decoded signal sequence; a matching unit, connected to the decoding unit, and configured to match the decoded signal sequence with a predetermined identification code to obtain a matched signal; a control unit, connected to the matching unit, and configured to generate one or more circuit adjusting parameters under the action of the matched signal.

Provided are an audio reconstruction method and device for providing improved sound quality by reconstructing a decoding parameter or an audio signal obtained from a bitstream, by using machine learning. The audio reconstruction method includes obtaining a plurality of decoding parameters of a current frame by decoding a bitstream, determining characteristics of a second parameter included in the plurality of decoding parameters and associated with a first parameter, based on the first parameter included in the plurality of decoding parameters, obtaining a reconstructed second parameter by applying a machine learning model to at least one of the plurality of decoding parameters, the second parameter, and the characteristics of the second parameter, and decoding an audio signal, based on the reconstructed second parameter.

Provided are an audio reconstruction method and device for providing improved sound quality by reconstructing a decoding parameter or an audio signal obtained from a bitstream, by using machine learning. The audio reconstruction method includes obtaining a plurality of decoding parameters of a current frame by decoding a bitstream, determining characteristics of a second parameter included in the plurality of decoding parameters and associated with a first parameter, based on the first parameter included in the plurality of decoding parameters, obtaining a reconstructed second parameter by applying a machine learning model to at least one of the plurality of decoding parameters, the second parameter, and the characteristics of the second parameter, and decoding an audio signal, based on the reconstructed second parameter.

An audio encoder for providing an output signal using an input audio signal includes a patch generator, a comparator and an output interface. The patch generator generates at least one bandwidth extension high-frequency signal, wherein a bandwidth extension high-frequency signal includes a high-frequency band. The high-frequency band of the bandwidth extension high-frequency signal is based on a low frequency band of the input audio signal. A comparator calculates a plurality of comparison parameters. A comparison parameter is calculated based on a comparison of the input audio signal and a generated bandwidth extension high-frequency signal. Each comparison parameter of the plurality of comparison parameters is calculated based on a different offset frequency between the input audio signal and a generated bandwidth extension high-frequency signal. Further, the comparator determines a comparison parameter from the plurality of comparison parameters, wherein the determined comparison parameter fulfills a predefined criterion.

An audio encoder for providing an output signal using an input audio signal includes a patch generator, a comparator and an output interface. The patch generator generates at least one bandwidth extension high-frequency signal, wherein a bandwidth extension high-frequency signal includes a high-frequency band. The high-frequency band of the bandwidth extension high-frequency signal is based on a low frequency band of the input audio signal. A comparator calculates a plurality of comparison parameters. A comparison parameter is calculated based on a comparison of the input audio signal and a generated bandwidth extension high-frequency signal. Each comparison parameter of the plurality of comparison parameters is calculated based on a different offset frequency between the input audio signal and a generated bandwidth extension high-frequency signal. Further, the comparator determines a comparison parameter from the plurality of comparison parameters, wherein the determined comparison parameter fulfills a predefined criterion.

The present disclosure provides methods, devices and computer program products for encoding and decoding of a vector of parameters in an audio coding system. The disclosure further relates to a method and apparatus for reconstructing an audio object in an audio decoding system. According to the disclosure, a modulo differential approach for coding and encoding a vector of a non-periodic quantity may improve the coding efficiency and provide encoders and decoders with less memory requirements. Moreover, an efficient method for encoding and decoding a sparse matrix is provided.

A microphone unit has a transducer, for generating an electrical audio signal from a received acoustic signal; a speech coder, for obtaining compressed speech data from the audio signal; and a digital output, for supplying digital signals representing said compressed speech data. The speech coder may be a lossy speech coder, and may contain a bank of filters with centre frequencies that are non-uniformly spaced, for example mel frequencies.