Abstract: A device, system, and method for machine-learning based noise suppression is provided. The device (and/or system) comprises a microphone, an output device, a noise suppression engine and a machine-learning noise suppression engine. The machine-learning noise suppression engine receives audio data from the noise suppression engine or the microphone, applies machine learning algorithms to the audio data to generate machine-learning based noise suppression parameters, and provides the parameters to the noise suppression engine. The noise suppression engine receives the audio data from the microphone and, prior to receiving the parameters, applies non-machine-learning based noise suppression to the audio data to generate noise-suppressed audio data, and provides the noise-suppressed audio data to the output device.

Abstract: A method and device for encoding a stereo sound signal comprise stereo encoders using stereo modes operating in time domain (TD), in frequency domain (FD) or in modified discrete Fourier transform (MDCT) domain. A controller controls switching between the TD, FD and MDCT stereo modes. Upon switching from one stereo mode to the other, the switching controller may (a) recalculate at least one length of down-processed/mixed signal in a current frame of the stereo sound signal, (b) reconstruct a down-processed/mixed signal and also other signals related to the other stereo mode in the current frame, (c) adapt data structures and/or memories for coding the stereo sound signal in the current frame using the other stereo mode, and/or (d) alter a TD stereo channel down-mixing to maintain a correct phase of left and right channels of the stereo sound signal. Corresponding stereo sound signal decoding method and device are described.

Abstract: Provided is an encoding apparatus for integrally encoding and decoding a speech signal and a audio signal, and may include: an input signal analyzer to analyze a characteristic of an input signal; a stereo encoder to down mix the input signal to a mono signal when the input signal is a stereo signal, and to extract stereo sound image information; a frequency band expander to expand a frequency band of the input signal; a sampling rate converter to convert a sampling rate; a speech signal encoder to encode the input signal using a speech encoding module when the input signal is a speech characteristics signal; a audio signal encoder to encode the input signal using a audio encoding module when the input signal is a audio characteristic signal; and a bitstream generator to generate a bitstream.

Abstract: Processor(s) of a client device can: receive audio data that captures a spoken utterance of a user of the client device; process, using an on-device speech recognition model, the audio data to generate a predicted textual segment that is a prediction of the spoken utterance; cause at least part of the predicted textual segment to be rendered (e.g., visually and/or audibly); receive further user interface input that is a correction of the predicted textual segment to an alternate textual segment; and generate a gradient based on comparing at least part of the predicted output to ground truth output that corresponds to the alternate textual segment. The gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model and/or is transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

Abstract: The present technology relates to a signal processing device, a method, and a program that can obtain a signal with higher sound quality. The signal processing device includes: a calculation unit that calculates a parameter for generating a difference signal corresponding to an input compressed sound source signal on the basis of a prediction coefficient and the input compressed sound source signal, the prediction coefficient being obtained by learning using, as training data, a difference signal between an original sound signal and a learning compressed sound source signal obtained by compressing and coding the original sound signal; a difference signal generation unit that generates the difference signal on the basis of the parameter and the input compressed sound source signal; and a synthesis unit that synthesizes the generated difference signal and the input compressed sound source signal. The present technology can be applied to a signal processing device.

Abstract: A method comprises determining a first modification weight according to linear spectral frequency (LSF) differences of the current frame and LSF differences of a previous frame of the current frame when a signal characteristic of the current frame meets a preset modification condition, modifying the linear predictive parameter of the current frame according to the determined first modification weight, and coding the current frame according to the modified linear predictive parameter.

Abstract: A device includes one or more processors configured to obtain sound information from an audio source. The one or more processors are further configured to select, based on a latency criterion associated with a playback device, a compression mode in which a representation of the sound information is compressed prior to transmission to the playback device or a bypass mode in which the representation of the sound information is not compressed prior to transmission to the playback device. The one or more processors are further configured to generate audio data that includes, based on the selected one of the compression mode or the bypass mode, a compressed representation of the sound information or an uncompressed representation of the sound information. The one or more processors are also configured to send the audio data as streaming data, via wireless transmission, to the playback device.

Abstract: The present application relates to a method of extracting audio features in a dialog detector in response to an input audio signal, the method comprising dividing the input audio signal into a plurality of frames, extracting frame audio features from each frame, determining a set of context windows, each context window including a number of frames surrounding a current frame, deriving, for each context window, a relevant context audio feature for the current frame based on the frame audio features of the frames in each respective context, and concatenating each context audio feature to form a combined feature vector to represent the current frame. The context windows with the different length can improve the response speed and improve robustness.

Abstract: An autocorrelation calculation unit 21 calculates an autocorrelation RO(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R?O(i) obtained by multiplying a coefficient wO(i) by the autocorrelation RO(i). It is assumed here, for each order i of some orders i at least, that the coefficient wO(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.

Abstract: An apparatus for decoding an audio signal is provided, having a receiving interface, configured to receive a first frame having a first audio signal portion of the audio signal, and configured to receive a second frame having a second audio signal portion of the audio signal; a noise level tracing unit, wherein the noise level tracing unit is configured to determine noise level information depending on at least one of the first audio signal portion and the second audio signal portion; a first reconstruction unit for reconstructing, in a first reconstruction domain, a third audio signal portion of the audio signal depending on the noise level information; a transform unit for transforming the noise level information to a second reconstruction domain; and a second reconstruction unit for reconstructing, in the second reconstruction domain, a fourth audio signal portion of the audio signal depending on the noise level information.

Abstract: A method and an apparatus for detecting correctness of a pitch period, where the method for detecting correctness of a pitch period includes determining, according to an initial pitch period of an input signal in a time domain, a pitch frequency bin of the input signal, where the initial pitch period is obtained by performing open-loop detection on the input signal, determining, based on an amplitude spectrum of the input signal in a frequency domain, a pitch period correctness decision parameter, associated with the pitch frequency bin, of the input signal, and determining correctness of the initial pitch period according to the pitch period correctness decision parameter.

Abstract: An audio processing system (100) accepts an audio bitstream having one of a plurality of predefined audio frame rates. The system comprises a front-end component (110), which receives a variable number of quantized spectral components, corresponding to one audio frame in any of the predefined audio frame rates, and performs an inverse quantization according to predetermined, frequency-dependent quantization levels. The front-end component may be agnostic of the audio frame rate. The audio processing system further comprises a frequency-domain processing stage (120) and a sample rate converter (130), which provide a reconstructed audio signal sampled at a target sampling frequency independent of the audio frame rate. By its frame-rate adaptability, the system can be configured to operate frame-synchronously in parallel with a video processing system that accepts plural video frame rates.

Abstract: Voice activity detection (VAD) is an enabling technology for a variety of speech based applications. Herein disclosed is a robust VAD algorithm that is also language independent. Rather than classifying short segments of the audio as either “speech” or “silence”, the VAD as disclosed herein employees a soft-decision mechanism. The VAD outputs a speech-presence probability, which is based on a variety of characteristics.

Abstract: The disclosure provides a speech processing method and a device thereof. The method includes: acquiring a speech sampling signal frame in a mixed-excitation linear prediction (MELP) speech coding system and estimating signal quality of the speech sampling signal frame; determining, based on the signal quality, a specific linear prediction coding (LPC) order used by an LPC circuit; controlling the LPC circuit to convert the speech sampling signal frame into a line spectrum pair parameter based on the specific LPC order; replacing a speech signal spectrum of the speech sampling signal frame with the line spectrum pair parameter to generate a predicted speech signal; and performing a speech coding operation and a signal synthesizing operation of the MELP speech coding system based on the predicted speech signal.

Abstract: An electronic device that includes a decompression engine that includes N decoders and a decompressor decompresses compressed input data that includes N streams of data. Upon receiving a command to decompress compressed input data, the decompression engine causes each of the N decoders to decode a respective one of the N streams from the compressed input data separately and substantially in parallel with others of the N decoders. Each decoder outputs a stream of decoded data of a respective type for generating commands associated with a compression standard for decompressing the compressed input data. The decompressor next generates, from the streams of decoded data output by the N decoders, commands for decompressing the data using the compression standard to recreate the original data. The decompressor next executes the commands to recreate the original data and stores the original data in a memory or provides the original data to another entity.

Abstract: An autocorrelation calculation unit 21 calculates an autocorrelation RO(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R?O(i) obtained by multiplying a coefficient wO(i) by the autocorrelation RO(i). It is assumed here, for each order i of some orders i at least, that the coefficient wO(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.

Abstract: A method of processing a signal, which includes receiving at least one of a first signal and a second signal, receiving mode information, and decoding the at least one of the first signal and the second signal using at least one of a first coding scheme and a second coding scheme according to the mode information. Further, the mode information is information for indicating that a prescribed mode corresponds to which one of at least three modes.

Abstract: A method and apparatus for compressing digital data, particularly audio and other data, in a way that the packing method used can be automatically detected and decoded at the receiving station. The audio signal is divided into compression packets consisting of four word pairs of left and right words. The first word pair in each compression packet is tagged with an identifier to indicate the start of a new compression packet, and is provided with configuration information which, over an entire compression block of 48 compression packets, constructs a 48-bit word specifying the manner in which the compressed audio and other data is packed.

Abstract: A method (100) includes receiving (101) an input digital audio signal comprising a narrow-band signal. The input digital audio signal is processed (102) to generate a processed digital audio signal. A high-band energy level corresponding to the input digital audio signal is estimated (103) based on a transition-band of the processed digital audio signal within a predetermined upper frequency range of a narrow-band bandwidth. A high-band digital audio signal is generated (104) based on the high-band energy level and an estimated high-band spectrum corresponding to the high-band energy level.

Abstract: There is provided a method of decoding speech data generated from a speech signal. The method comprises receiving the speech data having at least one main pulse in a subframe of the speech data; generating a first predicted pulse, based on the at least one main pulse, on one side of the main pulse in the subframe of the speech data, wherein the first predicted pulse has a lower gain than the main pulse; generating a second predicted pulse, as a mirror image of the first predicted pulse on a reverse time scale, on the other side of the main pulse in the subframe of the speech data; reconstructing the speech signal using the at least one main pulse, the first predicted pulse and the second predicted pulse.

Abstract: Provided are a classifying method and apparatus for an audio signal, and an encoding/decoding method and apparatus for an audio signal using the classifying method and apparatus. In the classification method, an audio signal is classified by adaptively adjusting a classification threshold for a frame of the audio signal that is to be classified according to a long-term feature of the audio signal, thereby improving a hit rate of signal classification, suppressing frequent mode switching per frame, improving noise tolerance, and providing smooth reconstruction of the audio signal.