A method for analyzing an acoustic signal having a time period and having a plurality of repeated audio patterns, has the following steps: receiving an audio signal having the acoustic signal; determining the audio patterns repeated within the acoustic signal; determining a window length for a plurality of windows, wherein the window length divides the time period of the acoustic signal into the plurality of windows; and windowing the acoustic signal to obtain the plurality of windows.

A psychology counseling device is provided. The device includes a user interface configured to receive an input from a user and provide information; a microphone configured to collect a voice of the user; a speaker configured to convey auditory information to the user; a processor configured to control the user interface, the microphone, and the speaker; and a memory accessible by the processor and configured to store executable instructions. The memory is configured to further store texts to be provided to the user and voice data received from the user. The executable instructions, when executed by the processor, causes the processor to perform: recognizing an emotional state of the user based on the user's input; providing texts including different contents to the user according to the emotional state of the user; receiving a voice that the user articulates the texts and storing the voice in the memory as the voice data; obtaining a plurality of modulated voices by converting the voice data; and providing at least two among the plurality of modulated voices to the user.

A method for continuous acoustic signature recognition and classification includes a step of obtaining an audio input signal from a resonant microphone array positioned proximate to a target, the audio input signal having a plurality of channels. The target produces characterizing audio signals depending on a state or condition of the target. A plurality of features is extracted from the audio input signal with a signal processor. The plurality of features is classified to determine the state of the target. An acoustic monitoring system implementing the method is also provided.

A method for continuous acoustic signature recognition and classification includes a step of obtaining an audio input signal from a resonant microphone array positioned proximate to a target, the audio input signal having a plurality of channels. The target produces characterizing audio signals depending on a state or condition of the target. A plurality of features is extracted from the audio input signal with a signal processor. The plurality of features is classified to determine the state of the target. An acoustic monitoring system implementing the method is also provided.

Using artificial intelligence and data observed using sensors or imaging devices to prompt a patient to provide responses or perform actions and then observing the patient's responses to the prompts and performing an assessment resulting in a quantitative result. The quantitative result is then used to complete a clinical qualitative assessment of the patient.

Using artificial intelligence and data observed using sensors or imaging devices to prompt a patient to provide responses or perform actions and then observing the patient's responses to the prompts and performing an assessment resulting in a quantitative result. The quantitative result is then used to complete a clinical qualitative assessment of the patient.

A dynamic neuropsychological assessment tool according to an embodiment utilizes speech recognition, speech synthesis and machine learning to assess whether a patient is at risk for a neurological disorder, such as Alzheimer's disease. The dynamic neuropsychological assessment tool enables self-administration by a patient. The tool performs pre-test validation operations on the test environment, test equipment, and the patient's capability for performing the test at that time. The tool also enables dynamic modification of a questionnaire presented to the patient while the patient completes the questionnaire. Also provides the dynamic modification of which tests to present the patient with. The modification can be rule based or modified by a provider. The dynamic neuropsychological assessment tool enables providers and administrators to modify and improve tests and validate them using machine learning based on previously completed assessments and results.

Embodiments herein determine when to place a passive assistive call using personal artificial intelligence (AI) assistants. The present embodiments improve upon the base functionalities of the assistant devices by monitoring the usually discarded or filtered-out environmental sounds to identify when a person is in distress to automatically issue an assistive call in addition to or alternatively to monitoring user speech for active commands to place assistive calls. The assistant device may be in communication with various other sensors to enhance or supplement the audio assessment of the persons in the environment, and may be used in a variety of scenarios where prior call systems struggled to quickly and accurately identify distress in various monitored persons (e.g., patients) including falls, stroke onset, and choking.

Embodiments herein determine when to place a passive assistive call using personal artificial intelligence (AI) assistants. The present embodiments improve upon the base functionalities of the assistant devices by monitoring the usually discarded or filtered-out environmental sounds to identify when a person is in distress to automatically issue an assistive call in addition to or alternatively to monitoring user speech for active commands to place assistive calls. The assistant device may be in communication with various other sensors to enhance or supplement the audio assessment of the persons in the environment, and may be used in a variety of scenarios where prior call systems struggled to quickly and accurately identify distress in various monitored persons (e.g., patients) including falls, stroke onset, and choking.

Aspects of the invention include a method including collecting, by a processor, physiological data from a user in an environment and a sound waveform from the user's environment. The method detects and labels as a potential annoyance, by the processor, a set of potential annoyance data based on the collected physiological data and the sound waveform. The method decomposes, by the processor, the sound waveform into a first sound waveform segment associated with the set of potential annoyance data and a second sound waveform segment not associated with the set of potential annoyance data. The method predicts, by the processor, that the potential annoyance is an actual annoyance. The method filters and modifies, by the processor, the first sound waveform segment associated with the actual annoyance and provides, by the processor, the second sound waveform segment not associated with the actual annoyance to the user.