Methods, systems, and storage media for augmenting a video are disclosed. Exemplary implementations may: receive a selection of an effect; receive user-generated content comprising video data and audio data; detect a characteristic of the audio data comprising at least a volume and/or a pitch of the audio data during a period of time; determine a series of numeric values based on the characteristic of the audio data during the period of time, individual numeric values of the series of numeric values being correlated with an amplitude of the volume and/or pitch at a discrete point within the period of time; and augment at least one of the video data and/or the audio data to include the effect based on the series of numeric values at discrete points in time within the period of time.

Methods, systems, and storage media for augmenting a video are disclosed. Exemplary implementations may: receive a selection of an effect; receive user-generated content comprising video data and audio data; detect a characteristic of the audio data comprising at least a volume and/or a pitch of the audio data during a period of time; determine a series of numeric values based on the characteristic of the audio data during the period of time, individual numeric values of the series of numeric values being correlated with an amplitude of the volume and/or pitch at a discrete point within the period of time; and augment at least one of the video data and/or the audio data to include the effect based on the series of numeric values at discrete points in time within the period of time.

Methods, apparatus and systems for radio-based voice activity detection are described. In one example, a described system comprises: a transmitter configured to transmit a radio signal through a wireless channel of a venue; a receiver configured to receive the radio signal through the wireless channel, wherein the wireless channel is impacted by a voice activity of a target voice source in the venue; and a processor. The processor is configured for: computing a time series of channel information (CI) of the wireless channel based on the radio signal, and detecting the voice activity of the target voice source based on the time series of CI (TSCI) of the wireless channel, without using any media signal.

Methods, apparatus and systems for radio-based voice activity detection are described. In one example, a described system comprises: a transmitter configured to transmit a radio signal through a wireless channel of a venue; a receiver configured to receive the radio signal through the wireless channel, wherein the wireless channel is impacted by a voice activity of a target voice source in the venue; and a processor. The processor is configured for: computing a time series of channel information (CI) of the wireless channel based on the radio signal, and detecting the voice activity of the target voice source based on the time series of CI (TSCI) of the wireless channel, without using any media signal.

Example embodiments relate to techniques for training artificial neural networks or oilier machine-learning encoders to accurately predict the pitch of input audio samples in a semitone or otherwise logarithmically-scaled pitch space. An example method may include generating, from a sample of audio data, two training samples by applying two different pitch shifts to the sample of audio training data. This can be done by converting the sample of audio data into the frequency domain and then shifting the transformed data. These known shifts are then compared to the predicted pitches generated by applying the two training samples to the encoder. The encoder is then updated based on the comparison, such that the relative pitch output by the encoder is improved with respect to accuracy. One or more audio samples, labeled with absolute pitch values, can then be used to calibrate the relative pitch values generated by the trained encoder.

Example embodiments relate to techniques for training artificial neural networks or oilier machine-learning encoders to accurately predict the pitch of input audio samples in a semitone or otherwise logarithmically-scaled pitch space. An example method may include generating, from a sample of audio data, two training samples by applying two different pitch shifts to the sample of audio training data. This can be done by converting the sample of audio data into the frequency domain and then shifting the transformed data. These known shifts are then compared to the predicted pitches generated by applying the two training samples to the encoder. The encoder is then updated based on the comparison, such that the relative pitch output by the encoder is improved with respect to accuracy. One or more audio samples, labeled with absolute pitch values, can then be used to calibrate the relative pitch values generated by the trained encoder.

A system (1) for analyzing an audio to measure oral reading fluency or progress in oral reading fluency (2) in a text illustrated through the audio. The system (1) includes an input unit (3) which receives a target audio (4) from a user. The target audio (4) relates to an oral reading of the text by the user. The system (1) further includes a transcribing unit (5) which receives and processes the target audio (4) and generates a target transcription (6) of the target audio (4). The system (1) also includes a processing unit (7) which receives and processes at least one of the target transcription (6), the text (8), the target audio (4), or a reference audio (9), or combination thereof, and generates a primary metrics (10) having various parameters measuring reading fluencies. The system supports user specific dictionary customization to incorporate non-dictionary words in the analysis.

A system (1) for analyzing an audio to measure oral reading fluency or progress in oral reading fluency (2) in a text illustrated through the audio. The system (1) includes an input unit (3) which receives a target audio (4) from a user. The target audio (4) relates to an oral reading of the text by the user. The system (1) further includes a transcribing unit (5) which receives and processes the target audio (4) and generates a target transcription (6) of the target audio (4). The system (1) also includes a processing unit (7) which receives and processes at least one of the target transcription (6), the text (8), the target audio (4), or a reference audio (9), or combination thereof, and generates a primary metrics (10) having various parameters measuring reading fluencies. The system supports user specific dictionary customization to incorporate non-dictionary words in the analysis.

Systems and methods are disclosed configured to detect impairment issues, and via an interlock device, inhibit operation of an item of equipment when impairment is detected. The interlock device may comprise a solid state relay, an electromechanical relay, and/or a solenoid. The interlock device may perform power isolation and/or may use a mechanism, such as a rotating cam or gear, to immobilize a control and/or other components. Based on detected impairment, a determination is made as to whether the interlock is to be activated or deactivated.

Systems and methods are disclosed configured to detect impairment issues, and via an interlock device, inhibit operation of an item of equipment when impairment is detected. The interlock device may comprise a solid state relay, an electromechanical relay, and/or a solenoid. The interlock device may perform power isolation and/or may use a mechanism, such as a rotating cam or gear, to immobilize a control and/or other components. Based on detected impairment, a determination is made as to whether the interlock is to be activated or deactivated.