Methods and systems are provided that provide visual display of audio signals in a video game. In one implementation, the system provides video game audio cues to deaf and hard of hearing players with audio cue reactive light emitting diode (LED) displays. These may be in the form of two lighting components placed on either side of a visual display. Audio cue reactive LED displays increase audio accessibility by converting audio stimuli into visual stimuli. Audio cue reactive LED displays can be attached to the left side and right side of any video game display, for example, to provide consistent sensory feedback that does not have to be in constant physical contact with deaf and hard of hearing players.

Aspects of the disclosure include computer-implemented methods and systems for providing generative adversarial network (GAN) digital image data. GAN digital image data corresponding to a suggested transaction for an identified customer can be determined.

A method comprises inputting an audio signal into a machine learning circuit to compress the audio signal into a sequence of actuator signals. The machine learning circuit being trained by: receiving a training set of acoustic signals and pre-processing the training set of acoustic signals into pre-processed audio data. The pre-processed audio data including at least a spectrogram. The training further includes training the machine learning circuit using the pre-processed audio data. The neural network has a cost function based on a reconstruction error and a plurality of constraints. The machine learning circuit generates a sequence of haptic cues corresponding to the audio input. The sequence of haptic cues is transmitted to a plurality of cutaneous actuators to generate a sequence of haptic outputs.

The present technology relates to an information processing apparatus, an information processing method, and a program which can curb occurrence of howling at the time of outputting vibration in response to an input sound. The information processing apparatus of one aspect of the present technology is an apparatus that generates, at the time of outputting vibration in response to an input sound from the outside, a vibration signal representing the vibration having a frequency different from a frequency of the input sound. The present technology can be applied to, for example, smartphones, smart watches, wearable apparatuses, cushions, and music experience apparatuses that vibrate in response to input sounds.

Embodiments of the present disclosure are directed to systems and methods for improving functional hearing. In one aspect, the system may include a housing configured to fit within an ear of a user. The housing may include a speaker, an amplifier, a transmitter, and a power supply. Additionally, the housing may include a memory storing instructions and at least one processor configured to execute instructions. The instructions may include receiving an audio input and amplifying the audio input. The instructions may include outputting the amplified audio input from a speaker. The instructions may include converting the audio input into a visual representation of the audio input and transmitting the visual representation to at least one display.

Technologies related to telecommunications are described herein, wherein such technologies are configured to assist users with hearing impairments. The technologies described herein cause transcriptions of spoken utterances directed to a telephone in a telephone conversation to be presented on a display of the telephone nearly simultaneously with the spoken utterances being audibly output by the telephone.

Systems and methods used in a cognitive assistant for detecting human emotions from speech audio signals is described. The system obtains audio signals from an audio receiver and extracts human speech samples. Subsequently, it runs a machine learning based classifier to analyze the human speech signal and classify the emotion observed in it. The user is then notified, based on their preferences, with a summary of the emotion detected. Notifications can also be sent to other systems that have been configured to receive them. Optionally, the system may include the ability to store the speech sample and emotion classification detected for future analysis. The system's machine learning classifier is periodically re-trained based on labelled audio speech data and updated.

The glasses with display may include a bridge, two temples hingedly coupled to the bridge, and a directional microphone array, the directional microphone array including two or more microphones positioned on the bridge or the temples. The glasses with display may also include a user microphone array, the user microphone array including one or more microphones positioned on the temples and oriented toward the mouth of a user wearing the glasses with display or one or more bone conduction microphones. In addition, the glasses with display include two lenses positioned in the bridge, at least one of the lenses including a display, the display visible by the user, the display including one or more of a directional display, closed caption display, and user volume display. The glasses with display additionally include a processor adapted to receive audio signals from the directional microphone array and the user microphone array, or from a separate mobile device, the processor adapted to control the display.

The glasses with display may include a bridge, two temples hingedly coupled to the bridge, and a directional microphone array, the directional microphone array including two or more microphones positioned on the bridge or the temples. The glasses with display may also include a user microphone array, the user microphone array including one or more microphones positioned on the temples and oriented toward the mouth of a user wearing the glasses with display or one or more bone conduction microphones. In addition, the glasses with display include two lenses positioned in the bridge, at least one of the lenses including a display, the display visible by the user, the display including one or more of a directional display, closed caption display, and user volume display. The glasses with display additionally include a processor adapted to receive audio signals from the directional microphone array and the user microphone array, or from a separate mobile device, the processor adapted to control the display.

Systems and Methods for assisting the hearing-impaired are described. The methods rely on obtaining audio signals from the ambient environment of a hearing-impaired person. The audio signals are analyzed by a machine learning model that can classify audio signals into audio categories (e.g. Emergency, Animal Sounds) and audio types (e.g. Ambulance Siren, Dog Barking) and notify the user leveraging a mobile or wearable device. The user can configure notification preferences and view historical logs. The machine learning classifier is periodically trained externally based on labelled audio samples. Additional system features include an audio amplification option and a speech to text option for transcribing human speech to text output.