Systems and methods for selecting and reporting objects based on events are provided. An indication of first and second objects, an indication of first events associated with the first object, and an indication of second events associated with the second object may be received. Based on the first events, it may be determined to include in a textual content a description based on the first events of the first object. Based on the second events, it may be determined not to include in the textual content any description based on the second events of the second object. Data associated with the first events may be analyzed to generate a particular description of the first object. The textual content including the particular description of the first object and not including any description based on the second events of the second object may be generated and provided.

Systems and methods for selecting and reporting objects based on events are provided. An indication of first and second objects, an indication of first events associated with the first object, and an indication of second events associated with the second object may be received. Based on the first events, it may be determined to include in a textual content a description based on the first events of the first object. Based on the second events, it may be determined not to include in the textual content any description based on the second events of the second object. Data associated with the first events may be analyzed to generate a particular description of the first object. The textual content including the particular description of the first object and not including any description based on the second events of the second object may be generated and provided.

This application relates to a howling suppression method and apparatus, a computer device, and a storage medium. The method includes obtaining a current audio signal corresponding to a current time period, and performing frequency domain transformation on the current audio signal; dividing the frequency domain audio signal and determining a target subband; obtaining a current howling detection result and a current voice detection result that correspond to the current audio signal, and determining a subband gain coefficient; obtaining a past subband gain corresponding to an audio signal within a past time period, and calculating a current subband gain corresponding to the current audio signal based on the subband gain coefficient and the past subband gain; and suppressing howling on the target subband based on the current subband gain, to obtain a first target audio signal corresponding to the current time period.

This application relates to a howling suppression method and apparatus, a computer device, and a storage medium. The method includes obtaining a current audio signal corresponding to a current time period, and performing frequency domain transformation on the current audio signal; dividing the frequency domain audio signal and determining a target subband; obtaining a current howling detection result and a current voice detection result that correspond to the current audio signal, and determining a subband gain coefficient; obtaining a past subband gain corresponding to an audio signal within a past time period, and calculating a current subband gain corresponding to the current audio signal based on the subband gain coefficient and the past subband gain; and suppressing howling on the target subband based on the current subband gain, to obtain a first target audio signal corresponding to the current time period.

This application provide an audio detection method performed by a computer device. The method includes: acquiring a target time point and a reference point of the target time point from target audio data; performing energy evaluation on the target time point according to an audio amplitude value of the target time point to obtain an energy evaluation value of the target time point; performing energy evaluation on the reference point according to an audio amplitude value of the reference point to obtain an energy evaluation value of the reference point; performing accuracy verification on the target time point according to the energy evaluation value of the target time point and the energy evaluation value of the reference point; and if the accuracy verification on the target time point succeeds, adding the target time point as a target stress point into a target stress point set.

The disclosure provides a multimodal speech recognition method and system, and a computer-readable storage medium. The method includes calculating a first logarithmic mel-frequency spectral coefficient and a second logarithmic mel-frequency spectral coefficient when a target millimeter-wave signal and a target audio signal both contain speech information corresponding to a target user; inputting the first and the second logarithmic mel-frequency spectral coefficient into a fusion network to determine a target fusion feature, where the fusion network includes at least a calibration module and a mapping module, the calibration module is configured to perform mutual feature calibration on the target audio/millimeter-wave signals, and the mapping module is configured to fuse a calibrated millimeter-wave feature and a calibrated audio feature; and inputting the target fusion feature into a semantic feature network to determine a speech recognition result corresponding to the target user. The disclosure can implement high-accuracy speech recognition.

Systems, methods and non-transitory computer readable media for analyzing objects data to generate a textual content reporting events are provided. An indication of an event may be received. An indication of a group of one or more objects associated with the event may be received. For each object of the group of one or more objects, data associated with the object may be received. The data associated with the group of one or more objects may be analyzed to select an adjective. A particular description of the event may be generated. The particular description may be based on the group of one or more objects. The particular description may include the selected adjective. A textual content may be generated. The textual content may include the particular description. The generated textual content may be provided.

A sound quality evaluation method and a sound quality evaluation system using same are provided. The sound quality evaluation system records playback of a test audio file on a plurality of playback devices to generate a plurality of pieces of audio data, and divides the audio data into a plurality of frequency bands. The sound quality evaluation system calculates the frequency bands to obtain a plurality of evaluation scores of the playback devices. The sound quality evaluation system captures sound quality ranking information corresponding to the playback devices from a reference source, and adjusts the evaluation scores according to the sound quality ranking information, to further obtain a reference model. The sound quality evaluation system adjusts correspondingly evaluation scores of a plurality of to-be-tested playback devices according to the reference model, to obtain sound quality ranking information of the to-be-tested playback devices.

A sound quality evaluation method and a sound quality evaluation system using same are provided. The sound quality evaluation system records playback of a test audio file on a plurality of playback devices to generate a plurality of pieces of audio data, and divides the audio data into a plurality of frequency bands. The sound quality evaluation system calculates the frequency bands to obtain a plurality of evaluation scores of the playback devices. The sound quality evaluation system captures sound quality ranking information corresponding to the playback devices from a reference source, and adjusts the evaluation scores according to the sound quality ranking information, to further obtain a reference model. The sound quality evaluation system adjusts correspondingly evaluation scores of a plurality of to-be-tested playback devices according to the reference model, to obtain sound quality ranking information of the to-be-tested playback devices.

Disambiguating question answering responses by receiving voice command data associated with a first user, determining a first user identity according to the first user voice command data, determining a first user activity context according to the first user voice command data, determining a first response for the first user, receiving voice command data associated with a second user, determining a second user identity according to the second user voice command data, determining a second user activity context according to the second user voice command data, determining a second response for the second user, determining a predicted ambiguity between the first response and the second response, altering the first response according to the predicted ambiguity, and providing the first response and the second response.