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{"gt_parse": {"c2t": "The block diagram represents a two-phase process for training and testing a machine learning system using lung sound data. Here's a detailed description of the diagram:1. **Training Phase (Phase 1):** - The process begins with the Training block, which indicates the initial phase where the machine learning model is trained. - Lung sound data is input into the system, which is then processed using the Mel frequency Cepstral Coefficient (MFCC) block. MFCC is a technique for extracting features from audio signals, which is particularly useful for voice recognition systems and, in this case, for analyzing lung sounds. - The features extracted by the MFCC are then fed into the Machine learning technique block. This block represents the algorithm or model that learns from the processed data. - During training, the model is provided with the Actual label for each piece of lung sound data. These labels are the correct answers or outcomes that the model is supposed to learn to predict.2. **Testing Phase (Phase 2):** - After the model has been trained, it enters the Testing phase. - During testing, new lung sound data is input into the system and again processed using the MFCC to extract relevant features. - These features are then passed to the trained machine learning model (the same Machine learning technique block as in the training phase). - The model now uses what it has learned to assign a Label to each piece of lung sound data, which is its prediction or classification of the data. - The predicted Label is then compared to the Actual label to determine if the model's prediction is correct. - The comparison results are used to calculate the Recognition Accuracy, which is a measure of how well the model is performing. This metric indicates the percentage of correct predictions made by the model on the testing data.The diagram uses solid lines to represent the flow of data and processes during both the training and testing phases. Dotted lines are used to delineate the two phases, with Phase 1-Training Phase and Phase 2-Testing Phase labels indicating the separation between them. The dotted lines also connect the Actual label to the Machine learning technique during training and to the Recognition Accuracy calculation during testing, highlighting the use of actual labels in both evaluating the model during training and testing its performance."}}