Abstract: Systems and methods of the present disclosure enable identifying labelling a source signal data signature using a computing system to test candidate chain oracle models by iteratively performing, for each particular number of neural network models in the range of the number of neural network models, a predetermined number of trials, where each trail includes: randomly selecting the particular number of neural network models; utilizing each neural network model of the particular number of neural network models to generate a respective predictive output based on the second input data; utilizing the LR model to generate a trial output based on the respective predictive output, and determining a model trial performance based on: the trial output, the second output data, and at least one machine learning performance metric. A chain oracle model from the candidate chain oracle models is determined based on the machine learning performance metric.

Abstract: Systems and methods use a processor to obtain a signal data signatures (SDS) of a forced cough vocalizations (FCV). The processor controls a display present an instruction to a user to produce an FCV. The processor controls a recording device to record audio during the FCV so as to receive an audio signal that captures the FCV. The processor pre-processes the audio signal and outputs FCV signal data representative of the FCV. The processor uses a cough signature model to ingest the FCV signal data and output an SDS representative of isolated FCV-related signal data. The processor validates one or more SDSs by classifying and testing the SDS against SDS criteria (baseline) that establish a minimum quality7 of the SDS. Based on the SDS failing to achieve the SDS criteria, the processor deletes the SDS and controls the display to present a new instruction to the user for a new FCV.

Abstract: Systems and methods of the present disclosure enable signal data signature detection using a memory unit and processor, where the memory using stores a computer program or computer programs created by the physical interface on a temporary basis. The computer program, when executed, cause the processor to perform steps to receive a signal data signature recording from at least one data source, receive a dataset of labeled signal data signature recordings including signal data signature recording labels, identify, using at least one machine learning model, boundaries within the dataset of labeled signal data signature recordings, classify the signal data signature recording to produce an output label using a compendium of signal data signature classifiers based on the boundaries within the dataset of labeled signal data signature recordings, determine an output type of the signal data signature recording, and display the output label on a display media.

Abstract: Systems and methods provide a HybridOps model for the identification, capture, isolation, feature engineering and adjudication of source signal data signatures for inclusion in calibration quality standard reference signal data signature libraries that improve machine learning and validation, reduces model bias and reduces model drift. The HybridOps model may include an “unlocked” AI/ML (machine learning enabled) public facing deployment pipeline in parallel with a clone AI/ML deployed in an internal development environment using a ML-Ops pipeline and in parallel with a clone “locked” AI/ML (machine learning disabled) as a standard reference. The three deployed models enables monitoring and measuring model drift, context drift and product progression for improved verification and validation of model reliability.

Abstract: Systems and methods of the present disclosure enable identifying labelling a source signal data signature using a computing system to test candidate chain oracle models by iteratively performing, for each particular number of neural network models in the range of the number of neural network models, a predetermined number of trials, where each trail includes: randomly selecting the particular number of neural network models; utilizing each neural network model of the particular number of neural network models to generate a respective predictive output based on the second input data; utilizing the LR model to generate a trial output based on the respective predictive output, and determining a model trial performance based on: the trial output, the second output data, and at least one machine learning performance metric. A chain oracle model from the candidate chain oracle models is determined based on the machine learning performance metric.

Abstract: Systems and methods of the present disclosure enable signal detection and/or recognition in audio recordings using one or more signal splitting techniques including a computing system configured therefor. The computing system may receive a signal data signature of time-varying data, the time-varying data having an event of interest and segment the signal data signature to isolate the event of interest by utilizing a first Hidden Markov model (HMM) configured to segment the signal data signature into at least one segment of the time-varying data by identifying state changes indicative of events of interest and where the at least one segment of the time-varying data has a first length. The computing system may use a second HMM configured to segment the at least one segment into a sub-segment of the time-varying data by identifying state changes within the at least one segment.

Abstract: Systems and methods provide a HybridOps model for the identification, capture, isolation, feature engineering and adjudication of source signal data signatures for inclusion in calibration quality standard reference signal data signature libraries that improve machine learning and validation, reduces model bias and reduces model drift. The HybridOps model may include an “unlocked” AI/ML (machine learning enabled) public facing deployment pipeline in parallel with a clone AI/ML deployed in an internal development environment using a ML-Ops pipeline and in parallel with a clone “locked” AI/ML (machine learning disabled) as a standard reference. The three deployed models enables monitoring and measuring model drift, context drift and product progression for improved verification and validation of model reliability.

Abstract: Systems and methods of the present disclosure enable signal data signature detection using a memory unit and processor, where the memory using stores a computer program or computer programs created by the physical interface on a temporary basis. The computer program, when executed, cause the processor to perform steps to receive a signal data signature recording from at least one data source, receive a dataset of labeled signal data signature recordings including signal data signature recording labels, identify, using at least one machine learning model, boundaries within the dataset of labeled signal data signature recordings, classify the signal data signature recording to produce an output label using a compendium of signal data signature classifiers based on the boundaries within the dataset of labeled signal data signature recordings, determine an output type of the signal data signature recording, and display the output label on a display media.

Abstract: Systems and methods of the present disclosure enable automated detection of signal data signatures by receiving a first reward and a first state including a signal data signature recording having a first onset location and a first offset location. An action is performed to produce a second state with second onset and offset locations based on the first state, the first reward and a policy of a reinforcement learning agent. A discriminator machine learning model determines a match score representative of a similarity between the second state and a target distribution of a signal data signature type. A second reward is determined based on the match score and, based on the second reward exceeding a threshold, a modified signal data signature recording is produced with the signal data signature having a modified beginning and a modified end according to the second onset location and the second offset location, respectively.