Performance Analysis of deep learning based speech enhancement model
Implementation Plan :
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Step 1: Initially, We load the diverse dataset of speech recordings with varying levels and types of noise (e.g., white noise, babble noise, street noise).
Step 2: Next, we perform the Preprocessing techniques, remove artifacts, normalize audio levels, and segment the data into appropriate training, validation, and testing sets.
Step 3: Next, we Define the neural network layers (e.g., CNN layers, RNN layers) and their configurations. Specify activation functions, regularization techniques, and optimization algorithms. Design a loss function that measures the quality of the enhanced speech.
Step 4: Next, we train the model on the prepared dataset while monitoring metrics such as loss and signal-to-noise ratio (SNR).
Step 5: Next we compare the results with state-of-the-art speech enhancement techniques and demonstrate how your model outperforms them.
Step 6: Finally, we evaluate the following performance metrics,like Signal-to-Noise Ratio (SNR), Perceptual Evaluation of Speech Quality (PESQ), Mean Opinion Score (MOS), Root Mean Square Error (RMSE), Computational Efficiency.
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Software Requirements:
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1. Tool: Python-3.11.3 or and above version
2. Language: Python
3. OS: Windows 10 – (64-bit)
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Note :-
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
