Electronics and Telecommunications Research Institute

Kyungpook National University Industry-Academic Cooperation Foundation

Young Ho Jeong

Tae Jin Lee

Soon Choul Kim

Jin Woo Hong

Soo In Lee

O Hyung Kwon

Bum Suk Choi

Jeoung Lak Ha

Jung Hak Kim

Soo Young Park

Dae Young Jang

Eun Jung Kwon

Seung Chul Kim

Jin Soo Choi

Heejeong Kim

Hui Yong Kim

Woo-taek Lim

Jae-hyoun Yoo

Han Seung Koo

Sang Won Suh

Yong Ju Lee

Seung Kwon Beack

Kyeongok Kang

Seokjin Lee

Minhan Kim

Jeong Ju Yoo

Tae Jung Kim

Chung Hyun Ahn

Chang Ki Kim

Seoung Chul Kim

Seungjae Baek

Seung-Hyeon Shin

A rendering method of an object-based audio signal and an apparatus for performing the same are provided. The rendering method of an object-based audio signal includes obtaining a rendered audio signal, performing clipping prevention on the rendered audio signal using a first limiter, mixing a signal output by the first limiter using a mixer, and performing clipping prevention on the mixed signal using a second limiter.

Rendering method of preventing object-based audio from clipping and apparatus for performing the same

A method of rendering object-based audio and an electronic device for performing the method are disclosed. The method includes identifying metadata of the object-based audio, determining whether the metadata includes a parameter set for an atmospheric absorption effect for each distance, and rendering the object-based audio, using a distance between the object-based audio and a listener obtained using the metadata and the atmospheric absorption effect according to an effect of a medium attenuation based on the parameter, when the metadata includes the parameter.

Method of rendering object-based audio and electronic device for performing the method

Disclosed are methods of training an acoustic scene classification model and classifying an acoustic scene and an electronic device for performing the methods. The training method of an acoustic scene classification model includes inputting training data labeled as an acoustic scene to the acoustic scene classification model that is repeatedly trained by using the training data and outputting a first result predicting the acoustic scene, updating the weight of the auxiliary model configured to induce training of the acoustic scene classification model, based on a weight of the acoustic scene classification model and a weight of an auxiliary model in a previous epoch, inputting the training data to the auxiliary model and outputting a second result, calculating a cost function, based on the first result, the second result, and labeling of acoustic data, and updating the weight of the acoustic scene classification model, based on the cost function.

Methods of training acoustic scene classification model and classifying acoustic scene and electronic device for performing the methods

Disclosed is a method and apparatus for label encoding in a multi-sound event interval. The method includes identifying an event interval in which a plurality of sound events occurs in a sound signal, separating a sound source into sound event signals corresponding to each sound event by performing sound source separation on the event interval, determining energy information for each of the sound event signals, and performing label encoding based on the energy information.

Method and apparatus for label encoding in polyphonic sound event intervals

Disclosed are a training method for a learning model for recognizing an acoustic signal, a method of recognizing an acoustic signal using the learning model, and devices for performing the methods. The method of recognizing an acoustic signal using a learning model includes identifying an acoustic signal including an acoustic event or acoustic scene, determining an acoustic feature of the acoustic signal, dividing the determined acoustic feature for each of a plurality of frequency band intervals, and determining the acoustic event or acoustic scene included in the acoustic signal by inputting the divided acoustic features to a trained learning model.

Training and learning model for recognizing acoustic signal

Disclosed is an apparatus and method for augmenting training data using a notch filter. The method may include obtaining original data, and obtaining training data having a modified frequency component from the original data by filtering the original data using a filter configured to remove a component of a predetermined frequency band.

Apparatus and method for augmenting training data using notch filter

Provided is a sound event recognition method that may improve a sound event recognition performance using a correlation between difference sound signal feature parameters based on a neural network, in detail, that may extract a sound signal feature parameter from a sound signal including a sound event, and recognize the sound event included in the sound signal by applying a convolutional neural network (CNN) trained using the sound signal feature parameter.

Method and apparatus for recognition of sound events based on convolutional neural network

Disclosed is a content processing method including receiving content including broadcast data and advertisement data into which additional information is inserted, extracting the additional information from the advertisement data, identifying the advertisement data from the content based on the extracted additional information, and extracting the broadcast data excluding the advertisement data identified from the content, wherein the additional information is inserted at at least one of optimal intervals determined based on test additional information inserted at a plurality of analysis intervals of an audio signal associated with the advertisement data.

Content processing method and system using audio signal of advertisement data

Provided are a signal processing method for determining an audience rating of media, and an additional information inserting apparatus, a media reproducing apparatus and an audience rating determining apparatus for performing the same method. In detail, the signal processing method for determining an audience rating of media is a method that may determine an audience rating of media with respect to a whole section of an audio signal by inserting additional information into a silence section through a noise signal.

Signal processing method for determining audience rating of media, and additional information inserting apparatus, media reproducing apparatus and audience rating determining apparatus for performing the same method

A method of detecting a sound event includes receiving sound signals using one or more directional microphones, extracting a time interval of each of the sound signals, extracting time information and an azimuth of a sound event included in the sound signals during the extracted time interval, mixing the sound signals received from the directional microphones using the extracted time interval, and determining a direction of the sound event generated at a specific time from a mixed sound signal obtained through the mixing using the extracted time information and azimuth of the sound event.

Method and apparatus for detecting sound event using directional microphone

Provided is a method and apparatus for synthesizing a separated sound source, the method including generating spatial information associated with a sound source included in a frame of a stereo audio signal, and synthesizing a separated frequency-domain sound source from the frame of the stereo audio signal based on the spatial information, wherein the spatial information includes a frequency-azimuth plane representing an energy distribution corresponding to a frequency and an azimuth of the frame of the stereo audio signal.

Method and apparatus for synthesizing separated sound source

Provided is an apparatus and method for creating multilingual audio content based on a stereo audio signal. The method of creating multilingual audio content including adjusting an energy value of each of a plurality of sound sources provided in multiple languages, setting an initial azimuth angle of each of the sound sources based on a number of the sound sources, mixing each of the sound sources to generate a stereo signal based on the set initial azimuth angle, separating the sound sources to play the mixed sound sources using a sound source separating algorithm, and storing the mixed sound sources based on a sound quality of each of the separated sound sources.

Apparatus and method of creating multilingual audio content based on stereo audio signal

Provided is a tag insertion method performed by an apparatus for inserting a tag into a stereo audio signal, the method including receiving an original stereo audio signal, analyzing an energy distribution of the original stereo audio signal based on an azimuth, determining valid azimuths for control information and for a plurality of pieces of tag information based on the energy distribution, wherein the control information is used to control tag information, modulating the plurality of pieces of tag information and the control information generated based on the valid azimuths, generating a left signal and a right signal based on the modulated control information and the plurality of pieces of modulated tag information, and generating a multi-tagged stereo audio signal by mixing the generated left signal and the generated right signal with the original stereo audio signal.

Method and apparatus for inserting tag into stereo audio signal, and method and apparatus for extracting tag from stereo audio signal

A broadcaster apparatus, content operator apparatus, and receiving terminal for augmented broadcasting service, and an augmented broadcasting service method. A service user may select a predetermined content operator among content operators that provide augmented content in an environment for providing an augmented broadcasting service according to an exemplary embodiment.

Broadcaster apparatus, content operator apparatus, and receiving terminal for augmented broadcasting service, and augmented broadcasting service method

A prediction mode determining method in scalable video coding based on a structure of multiple layers is provided. The prediction mode determining method includes: deriving a coded block pattern (CBP) value of a luma component for a 16&#xd7;16 mode of an encoding target picture; determining first candidate modes for the encoding target picture according to the derived CPB value; and determining a final prediction mode for the encoding target picture based on the determined first candidate modes. According to the present invention, encoding efficiency may be improved, and complexity may be reduced.

Growing community of inventors

Daejeon, South Korea

Young Ho Jeong