Panasonic Corporation

Google Inc.

Che-Ming Lin

Chia-Shin Yen

Weiming Liu

Qi-Sheng Liu

Chien-Ming Wu

Koichiro Mizushima

Yin-Shan Chen

A bidirectional media communication channel testing platform includes an HDMI testing device including a video input port and an audio output port; a plurality of media streaming devices, each including a video transmission channel and an audio return channel; and a bidirectional switch including a video path and an audio path. The video path is configured to selectively couple the video input port of the HDMI testing device to a video transmission channel of a selected one of the plurality of media streaming devices, and the audio path is configured to concurrently couple the audio output port of the HDMI testing device to the audio return channel of each of the plurality of media streaming devices, regardless of a switching state of the video path of the bidirectional switch.

Testing platform for HDMI enhanced audio return channel

A method and an apparatus for automatic noise compensation used with audio reproduction equipment are provided. The method comprises: (a) collecting a plurality of mixed audio signals, each mixed audio signal including equipment sound output by the audio reproduction equipment, and background noise; (b) removing the equipment sound from the mixed audio signals to obtain the background noise therein; (c) determining whether or not a plurality of mixed audio signals under inspection include a significant sound; and (d) adjusting the volume of the audio reproduction equipment according to whether or not a significant sound has been generated in the surrounding area and the magnitude of the background noise in the mixed audio signals under inspection to satisfy a plurality of predetermined compensation conditions.

Method and apparatus for automatic noise compensation used with audio reproduction equipment

An apparatus for detecting sound includes a plurality of microphones, a sound inspecting unit, a direction estimating unit, a background noise removing unit, and an alerting unit. The microphones are used to collect sounds around a user. The sound inspecting unit is used to calculate the feature values of a background noise within a preset time interval, and to determine if a latest collected sound satisfies a preset condition. When the preset condition is satisfied, the direction estimating unit is used to estimate the occurrence direction of the latest collected sound, and to determine if the occurrence direction is within a preset range behind the user. When the preset range is satisfied, the background noise removing unit is used to remove the background noise in the latest collected sound so as to obtain a detected sound. The alerting unit is used to inform the user of the detected sound via an alert message. A method for detecting sound is also disclosed.

Apparatus and method for detecting sound

A sound classification system for adding and correcting a sound type is disclosed. When the add/correct command processor receives a command to add or correct a sound type, the data in the first database is stored in the second database, and the type adding/correcting device adds the feature of the sound to the first database, and re-calculates the statistical values. Besides, the classifier re-classifies the sample sounds, and the precision calculator calculates a ratio of accurate classification. When the ratio is high, the type adding/correcting device stores, in the feature database, the feature of the sound for which a type is to be added or corrected. When the ratio is low, the second database restores the data back to the first database.

Sound classification system and method capable of adding and correcting a sound type

In a method and system for identifying speech sound and non-speech sound in an environment, a speech signal and other non-speech signals are identified from a mixed sound source having a plurality of channels. The method includes the following steps: (a) using a blind source separation (BSS) unit to separate the mixed sound source into a plurality of sound signals; (b) storing spectrum of each of the sound signals; (c) calculating spectrum fluctuation of each of the sound signals in accordance with stored past spectrum information and current spectrum information sent from the blind source separation unit; and (d) identifying one of the sound signals that has a largest spectrum fluctuation as the speech signal.

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Taipei, Taiwan

Che-Ming Lin