Abstract: The disclosure provides a method and a device in a User Equipment (UE) and a base station for wireless communication. A first node generates a first bit block, performs channel coding and then transmits a first radio signal. The first bit block comprising all bits in a second bit block and all bits in a third bit block is used for an input of the channel coding, and an output of the channel coding is used for generating the first radio signal. A Cyclic Redundancy Check (CRC) bit block of a fourth bit block is used for generating the third bit block. The fourth bit block comprises all bits in the second bit block and all bits in a fifth bit block, the bits in the fifth bit block are of fixed values, and the fifth bit block is composed of K bits, the K being a positive integer.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for discovering the presence of filler words through tokenization of a transcript derived from audio content. When audio content is obtained by a media production platform, the audio content can be converted into text content as part of a speech-to-text operation. The text content can then be tokenized and labeled using a Natural Language Processing (NLP) library. Tokenizing/labeling may be performed in accordance with a series of rules associated with filler words. At a high level, these rules may examine the text content (and associated tokens/labels) to determine whether patterns, relationships, verbatim, and context indicate that a term is a filler word. Any filler words that are discovered in the text content can be identified as such so that appropriate action(s) can be taken.

Abstract: Provided are a speech recognition device and method. The speech recognition device includes a microphone and a processor configured to receive a voice signal through the microphone, generate voice characteristic data by analyzing the voice signal by using a data recognition model based on a neural network, determine whether the voice signal is voice uttered from a user or voice output from an external device based on the voice characteristic data, and when the voice signal is determined as the voice uttered from the user, determine the voice signal as a voice command of the user and perform an operation corresponding to the voice command. The present disclosure also relates to an artificial intelligence (AI) system utilizing a machine learning algorithm such as deep learning, and applications thereof.

Abstract: A method and an apparatus for audio signal processing selection are provided. In the method, multiple audio signal processing operations are performed on a synthesized audio signal to generate multiple processed audio signals, the audio signal processing operations are evaluated according to the comparison results between the processed audio signals and the primary signal, and the audio signal processing operation corresponding to a designated application and the designated audio output mode is selected according to the evaluation result of the audio signal processing operations. The synthesized audio signal is generated by adding a secondary signal into a primary signal. The signal processing is related to remove the secondary signal from the synthesized audio signal. Those processed audio signals are used by the designated application at the designated audio output mode. The comparison result is related to signal similarity. The evaluation result is related to the highest signal similarity.

Abstract: Various aspects include active noise reduction (ANR) devices and approaches, one approach including: receiving an input signal representing audio captured by a feedforward microphone of an ANR headphone; receiving an error signal representing audio captured by an error measurement sensor; generating an anti-noise signal configured to reduce a noise signal over a frequency range; and applying a gain to at least one of the input signal or the anti-noise signal over the frequency range based on the error signal, wherein the applied gain is configured to enhance noise reduction for a plurality of users having distinct fits for the ANR headphone.

Abstract: The disclosure provides a method and a device in a User Equipment (UE) and a base station for wireless communication. A first node generates a first bit block, performs channel coding and then transmits a first radio signal. The first bit block comprising all bits in a second bit block and all bits in a third bit block is used for an input of the channel coding, and an output of the channel coding is used for generating the first radio signal. A Cyclic Redundancy Check (CRC) bit block of a fourth bit block is used for generating the third bit block. The fourth bit block comprises all bits in the second bit block and all bits in a fifth bit block, the bits in the fifth bit block are of fixed values, and the fifth bit block is composed of K bits, the K being a positive integer.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for discovering the presence of filler words through tokenization of a transcript derived from audio content. When audio content is obtained by a media production platform, the audio content can be converted into text content as part of a speech-to-text operation. The text content can then be tokenized and labeled using a Natural Language Processing (NLP) library. Tokenizing/labeling may be performed in accordance with a series of rules associated with filler words. At a high level, these rules may examine the text content (and associated tokens/labels) to determine whether patterns, relationships, verbatim, and context indicate that a term is a filler word. Any filler words that are discovered in the text content can be identified as such so that appropriate action(s) can be taken.

Abstract: The present disclosure relates to the field of medical data processing based on natural language processing. Embodiments of the present disclosure disclose a method and apparatus for verifying a medical fact. The method may include: acquiring a description text of the medical fact; selecting a relevant paragraph related to the description text of the medical fact from a medical document; and inputting the description text of the medical fact and the corresponding relevant paragraph into a trained discrimination model for authenticity judgment, to obtain a verification result of the medical fact, the discrimination model being pre-trained based on a medical text paragraph pair extracted from the medical document, and being iteratively adjusted using a medical fact sample set including authenticity labeling information after the pre-training.

Abstract: Various aspects include active noise reduction (ANR) devices and approaches, one approach including: receiving an input signal representing audio captured by a feedforward microphone of an ANR headphone; receiving an error signal representing audio captured by an error measurement sensor; generating an anti-noise signal configured to reduce a noise signal over a frequency range; and applying a gain to at least one of the input signal or the anti-noise signal over the frequency range based on the error signal, where the gain is calculated by: filtering the anti-noise signal over the frequency range to generate a filtered feedforward signal, and filtering the error signal over the frequency range to generate a filtered error signal; estimating a feedforward path contribution to the error signal; and determining the gain based on a correlation between the filtered error signal and the filtered feedforward signal with the assigned feedforward path contribution to the error signal.

Abstract: In one embodiment, a method includes, by one or more computing systems, receiving, from a client system, an input in a multi-turn message thread, parsing the input to identify a plurality of hypothesis dialog states associated with the input, generating a plurality of functions corresponding to the plurality of hypothesis dialog states, calculating a plurality of probability scores for the plurality of functions, respectively, based on a prior dialog state associated with the message thread and a current user context, wherein each probability score represents a likelihood that the respective function is associated with an intended dialog state, and storing the plurality of functions if each of the probability scores is below a threshold probability score, wherein the plurality of functions is stored as a hierarchical set of functions that is used as the prior dialog state in response to a next input in the message thread.

Abstract: In one embodiment, a method includes, by one or more computing systems, receiving, from a client system, an input in a multi-turn message thread, parsing the input to identify a plurality of hypothesis dialog states associated with the input, generating a plurality of functions corresponding to the plurality of hypothesis dialog states, calculating a plurality of probability scores for the plurality of functions, respectively, based on a prior dialog state associated with the message thread and a current user context, wherein each probability score represents a likelihood that the respective function is associated with an intended dialog state, and storing the plurality of functions if each of the probability scores is below a threshold probability score, wherein the plurality of functions is stored as a hierarchical set of functions that is used as the prior dialog state in response to a next input in the message thread.

Abstract: A system configured to vary a speech speed of speech represented in input audio data without changing a pitch of the speech. The system may vary the speech speed based on a number of different inputs, including non-audio data, data associated with a command, or data associated with the voice message itself. The non-audio data may correspond to information about an account, device or user, such as user preferences, calendar entries, location information, etc. The system may analyze audio data associated with the command to determine command speech speed, identity of person listening, etc. The system may analyze the input audio data to determine a message speech speed, background noise level, identity of the person speaking, etc. Using all of these inputs, the system may dynamically determine a target speech speed and may generate output audio data having the target speech speed.

Abstract: The present disclosure generally relates to interfaces and techniques for media playback on one or more devices. In accordance with some embodiments, an electronic device includes a display, one or more processors, and memory. The electronic device receives user input and, in response to receiving the user input, displays, on the display, a multi-device interface that includes: one or more indicators associated with a plurality of available playback devices that are connected to the device and available to initiate playback of media from the device, and a media playback status of the plurality of available playback devices.

Abstract: This invention relates to a feedback predictive controller, systems comprising and methods employing the same. Preferably the feedback predictive controller and/or systems comprising the feedback predictive controller are part of an automatic insulin delivery system. The methods described herein can be used to control blood glucose concentration in a patient with diabetes. Preferably, the insulin delivery system is an artificial pancreas.

Abstract: A query including a question from a user is received by a device. It is determined that the question and an associated answer is not found in a local cache of the device. At least one category for the question is determined. At least one cloud service for answering the question is determined. The at least one cloud service is in communication with the device via a network. A request including the question is sent to the at least one cloud service. Selecting a behavior is selected based upon the at least one category. The behavior includes a feedback to the user to reduce a perceived waiting time by the user. The behavior is executed.

Abstract: A first communication device includes a memory and a processor coupled to the memory. The processor is configured to receive, during a first call with a second communication device, an indication of a second call from a third communication device. The processor is further configured to monitor the first call, in response to the indication of the second call, for a pause in speech of at least a threshold duration. The processor is further configured to initiate, in response to detecting the pause in speech of at least the threshold duration, an audio message indicating call information associated with the second call.

Abstract: A method and an apparatus for estimating speech signal in split-domain is disclosed. The method includes performing LP analysis on a noisy speech signal to generate a first plurality of LPC and a first residual signal. The method also includes estimating speech LPC spectrum to generate cleaned LPC. The method further includes estimating speech residual spectrum to generate cleaned residual signal. The method also includes synthesizing output signals based on the cleaned LPC and the cleaned residual signal.

Abstract: Described are system, apparatus, article of manufacture, method, or computer program product embodiments for controlling streaming of media content. An embodiment operates by halting a presentation of future content from a buffer upon determining that the buffer is exhausted of content to present. The embodiment includes receiving one or more packets over a network connection, the one or more packets including media information corresponding to a first portion of streaming media content, in which the first portion corresponds to a second portion of the streaming media content. The one or more packets in a buffer are stored as buffered content. Responsive to determining that the network connection is not experiencing a burst condition, the buffer is trimmed. Then, presentation of buffered content is resumed and the first portion is caused to be presented in sync with the second portion.

Abstract: A method, system and computer program product for facilitating a meeting is implemented in a server computer and provides a graphical user interface to one or more client devices. The server displays a mind map of a center topic on the clients. The server collects statements of participants, and extracts words and phrases therefrom, to identify topics that are currently being discussed. The server determines whether a transition has occurred between the topics, and then calculates a correlation between the topics that are currently being discussed and the center topic. The server determines a location of the topics that are currently being discussed on the mind map, based on the calculated correlation, and displays an updated mind map on the clients. The server generates a warning on the clients when the server determines that the meeting proceedings are being retarded, based on the calculated correlation.

Abstract: An encoder for providing an audio stream on the basis of a transform-domain representation of an input audio signal includes a quantization error calculator configured to determine a multi-band quantization error over a plurality of frequency bands of the input audio signal for which separate band gain information is available. The encoder also includes an audio stream provider for providing the audio stream such that the audio stream includes information describing an audio content of the frequency bands and information describing the multi-band quantization error. A decoder for providing a decoded representation of an audio signal on the basis of an encoded audio stream representing spectral components of frequency bands of the audio signal includes a noise filler for introducing noise into spectral components of a plurality of frequency bands to which separate frequency band gain information is associated on the basis of a common multi-band noise intensity value.

Abstract: A comfort noise controller for generating CN (Comfort Noise) control parameters is described. A buffer of a predetermined size is configured to store CN parameters for SID (Silence Insertion Descriptor) frames and active hangover frames. A subset selector is configured to determine a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on residual energies. A comfort noise control parameter extractor (50B) is configured to use the determined CN parameter subset to determine the CN control parameters for a first SID frame following an active signal frame.

Abstract: A device performs a method for using image data to aid voice recognition. The method includes the device capturing (302) image data of a vicinity of the device and adjusting (304), based on the image data, a set of parameters for voice recognition performed by the device (102). The set of parameters for the device performing voice recognition include, but are not limited to: a trigger threshold of a trigger for voice recognition; a set of beamforming parameters; a database for voice recognition; and/or an algorithm for voice recognition. The algorithm may include using noise suppression or using acoustic beamforming.

Abstract: The disclosed embodiments illustrate a method for classifying one or more audio segments of an audio signal. The method includes determining one or more first features of a first audio segment of the one or more audio segments. The method further includes determining one or more second features based on the one or more first features. The method includes determining one or more third features of the first audio segment, wherein each of the one or more third features is determined based on a second feature of the one or more second features of the first audio segment and at least one second feature associated with a second audio segment. Additionally, the method includes classifying the first audio segment either in an interrogative category or a non-interrogative category based on one or more of the one or more second features and the one or more third features.

Abstract: Systems and methods for developing and utilizing a contactability profile are disclosed. Contact information may be managed by receiving activity data associated with an entity, processing the received activity data, generating at least one contactability profile associated with the entity based upon a result of the processing, and storing the generated contactability profile.

Abstract: A beamformer system that can isolate a desired portion of an audio signal resulting from a microphone array. A combination of beamformers is used to dampen undesired noise, whether diffuse or coherent. A fixed beamformer is used to dampen diffuse noise while an adaptive beamformer is used to cancel directional coherent noise. The adaptive beamformer isolates and weights audio from various directions. The weights may vary depending on the isolated desired audio signal, dynamically adjusting the step-size adjustments to the weights.

Abstract: A method for using oral feedback to facilitate user selection among a plurality of service devices. The method comprises a plurality of service devices. Each service device has a dynamically-updatable quality control profile. A first service device receives a request from the first user to perform a first service job. The first service device performs the first service job. A first recording device located proximate to the first service device obtains oral feedback from the first user regarding the quality of the first service job. The first service device analyzes the oral feedback using natural language processing to form an analysis. The first service device modifies the first quality control profile of the first service device based on the analysis.

Abstract: An apparatus for encoding audio information is provided. The apparatus for encoding audio information includes a selector for selecting a comfort noise generation mode from two or more comfort noise generation modes depending on a background noise characteristic of an audio input signal, and an encoding unit for encoding the audio information, wherein the audio information includes mode information indicating the selected comfort noise generation mode.

Abstract: A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

Abstract: When removing a block distortion occurring in a local decoded image, a loop filtering part 11 of an image coding device carries out a filtering process on each of signal components (a luminance signal component and color difference signal components) after setting the intensity of a filter for removing the block distortion for each of the signal components according to a coding mode (an intra coding mode or an inter coding mode) selected by a coding controlling part 1.

Abstract: Described herein is a MEMS acoustic transducer device provided with a micromechanical detection structure that detects acoustic-pressure waves and supplies a transduced electrical quantity, and with an integrated circuit operatively coupled to the micromechanical detection structure and having a reading module that generates at output an audio signal as a function of the transduced electrical quantity. The integrated circuit is further provided with a recognition module, which recognizes a sound activity event associated to the transduced electrical quantity. The MEMS acoustic transducer has an output that supplies at output a data signal that carries information regarding recognition of the sound activity event.

Abstract: An automatic speech recognition engine and a method of using the engine is described. The method pertains to front-end processing an audio signal and includes the steps of: identifying a plurality of voiced-frames of the audio signal; determining that one or more of the plurality of voiced-frames have a signal-to-noise (SNR) value greater than a first predetermined threshold; and based on the determination, bypassing noise suppression for the one or more of the plurality of voiced-frames.

Abstract: Methods and apparatus for reducing power consumption in a wireless device operating in a discontinuous transmission (DTX) mode while using a voice over Long Term Evolution (VoLTE) service. The wireless device detects a period of voice inactivity and transmits one or more silence descriptor (SID_UPDATE) frames to a second wireless device in place of encoded speech frames. The SID_UPDATE frames are transmitted periodically based on measurements of comfort noise parameters. The wireless device determines a difference between weighted averages of comfort noise (CN) parameters of two sequences of encoded speech frames. When the difference exceeds a difference threshold, a SID_UPDATE frame is transmitted. Additionally, in some embodiments, a SID_UPDATE frame is transmitted when the weighted average of CN parameters exceeds a parameter threshold and/or when a time between SID_UPDATE frames or time elapsed after entering a silence state exceeds one or more time thresholds.

Abstract: An encoding apparatus and a decoding apparatus in a transform between a Modified Discrete Cosine Transform (MDCT)-based coder and a different coder are provided. The encoding apparatus may encode additional information to restore an input signal encoded according to the MDCT-based coding scheme, when switching occurs between the MDCT-based coder and the different coder. Accordingly, an unnecessary bitstream may be prevented from being generated, and minimum additional information may be encoded.

Abstract: A voice decoding apparatus includes an MBE-type decoder, a sampling convertor, a non-linear components generator and an adder. The decoder decodes digital voice-encoded information to generate a first decoded voice signal. The convertor converts the first decoded voice signal to a second decoded voice signal with a higher sampling frequency. The generator performs a non-linear process to the first or second decoded voice signal to generate an additional voice signal with the same sampling frequency as the second decoded voice signal. The additional voice signal has components in a frequency band in which the first decoded voice signal has no component and continuing to another frequency band of the first decoded voice signal. The adder adds the second decoded voice signal to the additional voice signal.

Abstract: In an embodiment, bitstream elements of sub-frames are encoded differentially to a global gain value so that a change of the global gain value results in an adjustment of an output level of the decoded representation of the audio content. Concurrently, the differential coding saves bits. Even further, the differential coding enables the lowering of the burden of globally adjusting the gain of an encoded bitstream. In another embodiment, a global gain control across CELP coded frames and transform coded frames is achieved by co-controlling the gain of the codebook excitation of the CELP codec, along with a level of the transform or inverse transform of the transform coded frames. In another embodiment, the gain value determination in CELP coding is performed in the weighted domain of the excitation signal.

Abstract: An encoder for providing an audio stream on the basis of a transform-domain representation of an input audio signal includes a quantization error calculator configured to determine a multi-band quantization error over a plurality of frequency bands of the input audio signal for which separate band gain information is available. The encoder also includes an audio stream provider for providing the audio stream such that the audio stream includes information describing an audio content of the frequency bands and information describing the multi-band quantization error. A decoder for providing a decoded representation of an audio signal on the basis of an encoded audio stream representing spectral components of frequency bands of the audio signal includes a noise filler for introducing noise into spectral components of a plurality of frequency bands to which separate frequency band gain information is associated on the basis of a common multi-band noise intensity value.

Abstract: An information processing apparatus includes an audio processing unit that performs a predetermined process for an input audio signal, and a power saving control unit that allows the audio processing unit to transition into a power saving state, depending on the length of a silence section in the audio signal.

Abstract: A decoding apparatus includes a sound code input unit that receives a sound code output from an encoding apparatus through a sound wave reception device; a frame division unit that divides the sound code depending on a predetermined time interval to generate a plurality of frames; a frequency identification unit that identifies a frequency corresponding to each of the plurality of the frames through frequency analysis for each of the plurality of the frames; and an information generation unit that determines a frequency band, to which each of the identified frequencies corresponds, from an audible sound wave frequency band and a non-audible sound wave frequency band, and a plurality of partial information based on the frequency band and each of the identified frequencies, and generates information corresponding to the sound code based on the plurality of the partial information.

Abstract: Speech recognition systems often process speech by employing models and analyzing audio data. An embodiment of the method and corresponding system described herein allow for passive monitoring of, for example, conversation between user(s) to determine context to use to prime model(s) for later speech recognition requests submitted to the speech recognition system. The embodiment improves the results of the speech recognition system by updating speech recognition model(s) with contextual information of the conversation. This increases the probability that the speech recognition system interprets the conversation to contextually relevant information.

Abstract: Acoustic noise in an audio signal is reduced by calculating a speech probability presence (SPP) factor using minimum mean square error (MMSE). The SPP factor, which has a value typically ranging between zero and one, is modified or warped responsive to a value obtained from the evaluation of a sigmoid function, the shape of which is determined by a signal-to-noise ratio (SNR), which is obtained by an evaluation of the signal energy and noise energy output from a microphone over time. The shape and aggressiveness of the sigmoid function is determined using an extrinsically-determined SNR, not determined by the MMSE determination. The extrinsically-determined SNR is obtained from a long term history of previously-determined speech presence probabilities and a long term history of previously-determined noise histories.

Abstract: A method for processing audio data includes determining a first common scalefactor value for representing quantized audio data in a frame. A second common scalefactor value is determined for representing the quantized audio data in the frame. A line equation common scalefactor value is determined from the first and second common scalefactor values.

Abstract: A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

Abstract: A method includes, during operation of a mobile handset when a voice call is in progress with a remote party, detecting an occurrence of a start of a discontinuous transmission period that results from a cessation of speech by the remote party; and activating a visual indicator of the mobile handset to assume a first state that indicates that a speech signal is not being received from the remote party.

Abstract: A method, system and apparatus are shown for identifying non-language speech sounds in a speech or audio signal. An audio signal is segmented and feature vectors are extracted from the segments of the audio signal. The segment is classified using a hidden Markov model (HMM) that has been trained on sequences of these feature vectors. Post-processing components can be utilized to enhance classification. An embodiment is described in which the hidden Markov model is used to classify a segment as a language speech sound or one of a variety of non-language speech sounds. Another embodiment is described in which the hidden Markov model is trained using discriminative learning.

Abstract: A Voice Activity Detection/Silence Suppression (VAD/SS) system is connected to a channel of a transmission pipe. The channel provides a pathway for the transmission of energy. A method for operating a VAD/SS system includes detecting the energy on the channel, and activating or suppressing activation of the VAD/SS system depending upon the nature of the energy detected on the channel.

Abstract: A noise filler for providing a noise-filled spectral representation of an audio signal on the basis of an input spectral representation of the audio signal has a spectral region identifier configured to identify spectral regions of the input spectral representation spaced from non-zero spectral regions of the input spectral representation by at least one intermediate spectral region, to obtain identified spectral regions, and a noise inserter configured to selectively introduce noise into the identified spectral regions to obtain the noise-filled spectral representation of the audio signal. A noise filling parameter calculator for providing a noise filling parameter on the basis of a quantized spectral representation of an audio signal has a spectral region identifier, as mentioned above, and a noise value calculator configured to selectively consider quantization errors of the identified spectral regions for a calculation of the noise filling parameter.

Abstract: Embodiments of the present invention relate to a voice detector receiving an input signal that is divided into sub-signals that represent a frequency sub-band. The voice detector calculates, for each sub-band, a signal-to-noise (SNR) value based on a corresponding sub-signal for each sub-band and a background signal for each sub-band. The voice detector also calculates a power SNR value for each sub-band, where at least one of the power SNR values is calculated based on a non-linear function. The voice detector forms a single value based on the calculated power SNR values and compares the single value and a given threshold value to make a voice activity decision presented on an output port.

Abstract: Voice activity detection using multiple microphones can be based on a relationship between an energy at each of a speech reference microphone and a noise reference microphone. The energy output from each of the speech reference microphone and the noise reference microphone can be determined. A speech to noise energy ratio can be determined and compared to a predetermined voice activity threshold. In another embodiment, the absolute value of the autocorrelation of the speech and noise reference signals are determined and a ratio based on autocorrelation values is determined. Ratios that exceed the predetermined threshold can indicate the presence of a voice signal. The speech and noise energies or autocorrelations can be determined using a weighted average or over a discrete frame size.

Abstract: There is provided an audio coding device which appropriately sets the quantization bit number by a small calculation amount in each stage when coding an input audio signal by performing multi-stage normalization/quantization. A quantization information calculation section determines total quantization information idwl0, based on normalization information idsf, and allocates the total quantization information idwl0 for quantization information idwl1 and quantization information idwl2. At this time, the quantization information calculation section limits the quantization information idwl1 by a limiter lim1, and allocates the total quantization information idwl0 for quantization information idwl1. If the quantization information idwl1 exceeds the limiter lim1, the excess is allocated for the quantization information idwl2. A first normalization section and a first quantization section normalizes and quantizes a frequency spectrum mdspec1 in the first stage.

Abstract: There is provided an audio coding device which appropriately sets the quantization bit number by a small calculation amount in each stage when coding an input audio signal by performing multi-stage normalization/quantization. A quantization information calculation section determines total quantization information idwl0, based on normalization information idsf, and allocates the total quantization information idwl0 for quantization information idwl1 and quantization information idwl2. At this time, the quantization information calculation section limits the quantization information idwl1 by a limiter lim1, and allocates the total quantization information idwl0 for quantization information idwl1. If the quantization information idwl1 exceeds the limiter lim1, the excess is allocated for the quantization information idwl2. A first normalization section and a first quantization section normalizes and quantizes a frequency spectrum mdspec1 in the first stage.